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Andrew Roy Legal assists tech-enabled startups throughout their journey from incorporation to IPO. Get in touch today to discover how we can support you. Welcome to Part 4 of our series, Legal Fundamentals for Canadian Technology Startups. Our aim is to provide a practical legal roadmap for launching your Canadian tech startup. Don't miss out on Part 1 Exploring Business Structures for Canadian Tech Startups: Sole Proprietorship and Partnership , Part 2 Exploring Business Structures for Canadian Tech Startups: The Corporation , and Part 3 Seed Financing 101: Understanding SAFEs and Convertible Notes for Tech Startups Our series has so far explored financial instruments crucial for startup growth phases, including SAFEs and Convertible Notes. These instruments are essential in early stages where a startup’s valuation is yet to be established. We now shift our focus to a cornerstone of later-stage financing: priced rounds and preferred shares. Understanding the Startup Financing Ecosystem: Trends and Insights A deep dive into priced rounds and preferred share financing necessitates an understanding of the broader Canadian tech startup ecosystem. Despite a 34% decrease in venture capital investment to $6.9 billion across 660 deals in 2023, data from the Canadian Venture Capital and Private Equity Association (CVCA) shows that investment levels remain higher than those recorded in 2019 and 2020. This trend indicates a cautious but significant commitment within the VC sector, suggesting a gradual realignment with pre-pandemic investment patterns. Kim Furlong, CEO of CVCA, anticipates a continued focus on AI and cleantech sectors , along with a careful approach to VC investing in 2024. The emphasis on profitability over mere growth indicates a mature, discerning investment landscape. AI investments, in particular, are highlighted as a strategic priority. For entrepreneurs exploring fertile financing avenues, YC Combinator's Request for Startups identifies 20 sought-after concepts, with AI in Robotics taking the lead. This is exemplified by Figure, a robotics company, securing $675 million at a $2.6 billion valuation . For more insights into VC trends, consider following my LinkedIn for daily updates. Now, let’s delve into Startup law 101: priced rounds and preferred share financing. Startup Law 101: Priced Rounds Explained Priced rounds represent a crucial milestone in a startup's funding lifecycle, typically occurring when investments exceed $2M . These rounds involve significant changes in terms and financing structures: Series A is initiated once a startup demonstrates measurable traction, evidenced through revenue generation and customer base expansion. Series B financing is typically sought when a startup has successfully established product-market fit. Series C often represents the final financing stage before a startup contemplates an Initial Public Offering (IPO) or acquisition. Series D may be pursued to enhance valuation pre-IPO or to capitalize on emerging opportunities requiring additional funding.
At these stages, a startup undergoes "pricing," where it negotiates and establishes a valuation on its company. This valuation in turn activates any preceding SAFEs or convertible notes from pre-seed or seed financing rounds . Unlike earlier rounds, however, preferred shares are the legal instruments used for priced rounds. Venture Capitalists (VCs), who generally lead these later rounds, insist on these preferred shares because of the enhanced protections that they afford in terms of equity ownership. Preferred shares are expensive because they need to be closely negotiated and entail significant legal documentation. Startup Law 101: Delving into Preferred Shares Preferred shares differ from common stock or shares because they offer some preferential treatment to the investor. These “preferences” can be manifested in different ways and include a variety of different rights. While this list is not meant to be exhaustive, some of the more common rights are as follows: Liquidation Preferences : Investors often negotiate liquidation preferences to ensure they recoup their investment before other shareholders in the event of a sale or liquidation. The current market tends to set the liquidation preference at "1x, non-participating," which means that investors get back their investment amount but do not participate in further distributions. Anti-dilution Protections : To protect against dilution in future funding rounds, investors will negotiate anti-dilution clauses. These provisions adjust the conversion price of preferred shares if new shares are issued at a lower price, ensuring investors maintain their ownership percentage​ Conversion Rights : Preferred shares are often convertible into common shares at the investor's discretion, potentially upon specific triggers such as an IPO or by mutual agreement​. Optional conversion rights are generally non-negotiable and allow an investor to convert their share of preferred stock into common stock on a one-to-one-basis. Dividend Rights : While not always a focus in startup financing , preferred shares may have provisions for dividends, which are usually non-cumulative and paid only if declared by the board​ Voting Rights : These rights modify the voting rights of the preferred shareholder relative to common shareholders and contingent upon agreement and negotiation. As for what's market, VCs often negotiate for terms where all shares vote together as a single class on an "as-converted basis." As-converted basis means they do not require separate approval from preferred stockholders to approve changes like an increase in authorized common shares. Protective Provisions : These provisions typically require that a certain percentage (often a majority, but sometimes a supermajority) of the preferred stockholders vote to approve certain actions. Some actions that are typically included are dissolving the company, taking on debt, and changing the size of the board. Drag-along Rights : These rights enable majority shareholders to force minority shareholders to join in the sale of a company, ensuring all can benefit from a collective sale. The Term Sheet These preferential terms and conditions are included in a term sheet, which is the most important document that is negotiated during a priced round. The details of how to properly negotiate a term sheet is significant. The blog Strictly Business put out a comprehensive 20 part series on this matter. On the other hand, if you are a visual learner and would like to see a typical model document of a term sheet, the Canadian Venture Capital & Private Equity Association provides a free model term sheet , updated to November 2020. Pro forma cap table When negotiating a term sheet, you should also be developing a pro forma cap table for the entire round. The pro forma cap table is designed to show you what your equity looks like post-financing. It’s advisable to built it up at the term sheet stage because, as negotiations evolve, you can see the impact of the adjustments on your equity interest post-closing. A pro forma cap table will normally include the shareholder, their title, the amount of stock owned, the type of stock owned, the equivalent of the preferred stock in common stock, the full diluted percentage owned, and stock options and warrants. A few examples of pro forma cap tables can be readily found here and here . Conclusion Navigating the intricate landscape of venture capital financing, particularly when it comes to the nuanced world of preferred shares and priced rounds, requires a strategic blend of legal insight, market understanding, and negotiation acumen. For Canadian technology startups poised at the brink of significant growth phases, comprehending the mechanics of these financial instruments is not just beneficial—it's imperative for forging successful paths forward. By equipping yourself with this knowledge and partnering with seasoned legal advisors like Andrew Roy Legal, you're setting the stage for informed decision-making that aligns with your startup's vision and financial realities. As the Canadian tech ecosystem continues to evolve, staying informed and agile will be key to navigating the venture capital landscape, maximizing growth opportunities, and steering your startup towards a prosperous future. Reach out today for a free consultation and see how we can help you. This article is intended for informational purposes only and should not be considered as legal advice and does not establish an attorney-client relationship. Consulting with a qualified legal professional is recommended for specific legal concerns and requirements related to your business. © 2024 Andrew Roy

Andrew Roy Legal assists tech-enabled startups throughout their journey from incorporation to IPO. Get in touch today to discover how we can support you. Welcome to Part 3 of our series, Legal Fundamentals for Canadian Technology Startups. We're here to offer a pragmatic, legal roadmap for launching your Canadian tech startup. Be sure to check out Part 1 Exploring Business Structures for Canadian Tech Startups: Sole Proprietorship and Partnership and Part 2 Exploring Business Structures for Canadian Tech Startups: The Corporation . Introduction In the dynamic realm of tech-enabled startups, securing financing is a pivotal driver for expansion, especially when innovative ideas surpass available capital. The initial funding phase, often termed the "seed" round, establishes the groundwork for subsequent financing rounds like "Series A," "Series B," and beyond (terminology variations notwithstanding, a "seed" round in this article denotes a startup's first formal financing stage). This discussion delves into the two prevalent legal instruments for seed stage financing: the SAFE and the Convertible Note. Additionally, it examines a crucial term within these instruments: the Valuation Cap. The Valuation Problem for Startup Financing In many industries, financing typically follows the paths of either debt or equity . Debt manifests as loans, lines of credit, or bonds and debentures, while equity is commonly represented by shares , whether common or preferred . However, in the unique landscape of seed financing for tech-enabled startups, these traditional avenues are not viable due to the challenges posed by the valuation problem. In conventional finance and accounting, valuation of companies are typically evaluated using established methods, such as assessing earning multiples , determining the fair market value of assets , or considering the value of industry competitors . However, early-stage startups, often operating in pre-revenue stages and lacking substantial assets, face challenges, especially in markets without established industry benchmarks. This makes traditional valuation methods impractical, creating difficulty in "pricing" the company to a level where it can attract traditional debt and equity financing. To tackle this valuation problem, the startup industry has chosen a deferred approach. Valuation is postponed until the startup reaches a stage where it has developed adequate revenue, assets, or competition to establish a meaningful valuation. This deferral is facilitated by relying on two key financing instruments for seed financing: the convertible note and the SAFE. The Convertible Note A convertible note functions as a hybrid legal instrument, blending characteristics of both debt and equity. It embodies debt traits with a maturity date , interest accrual , and a principal amount , and yet, it transitions into equity by converting into company shares at a future date. This conversion is typically linked to a liquidity event such as a priced equity financing round, the sale of the company, or an initial private offering (IPO). The convertible note, in effect, defers a formal evaluation until a later time while allowing for investment in the corporation. Convertible notes allow the investor the ability to “lock in” a conversion formula for their investment that will yield a better conversion price for the investment than if those same investors invested their money in the “priced” rounds (more on that idea below). For founders, a convertible note facilitates faster, cheaper, and more flexible access to financing compared to raising funds during a priced round, which normally require issuing preferred shares . It also ensures that investors do not become shareholders until the instrument converts to shares, reducing administrative and legal burdens for the company until later stages. Terms of convertible notes vary significantly based on the company, investor, and legal counsel involved. Sophisticated investors often have their own standard or precedent convertible notes, which include terms related to interest, conversion to equity, security grants, representations and warranties, permissible amendments, and additional investor rights. SAFEs The second prevalent legal instrument in seed financing is the Simple Agreements for Future Equity (SAFE) , introduced by Y Combinator and widely adopted in the United States and Canada for seed-stage financing. Designed with startup friendliness in mind, SAFEs differ from convertible notes by functioning neither as debt nor equity. Instead, they operate as promises for future equity, contingent on terms outlined in the SAFE. Investors inject capital into the startup, receiving a commitment of future equity upon the occurrence of a future equity financing (i.e. a Series A round ) or another liquidity event , such as the sale of the company. Similar to convertible notes, the valuation of the company isn't mandatory at the time of SAFE issuance. SAFEs boast several advantages. Their standardization reduces legal fees and negotiation costs for both investors and startups. They require less documentation and can be closed individually rather than simultaneously with multiple investors. Moreover, as non-debt instruments, SAFEs lack maturity dates or interest rates, allowing startups to grow without the pressure of repaying debts by specific deadlines. Valuation Caps and Discount Rates As we delve into the intricacies of seed financing, one may question why investors would willingly embrace the risk associated with early-stage startups, where an estimated 75% of financing may not yield positive returns . The answer lies in the intentional design of mechanisms like valuation caps and discount rates, offering tangible benefits for early investors despite the inherent risk. Valuation Cap The valuation cap is the most important term in the SAFE and the Convertible Note. Valuation caps could also be the most complicated term of seed financing. A valuation cap sets the “maximum valuation – for the purposes of determining the price per share – at which an investor’s money converts into equity, even if investors in the priced round agree to and pay a different price per share based on a higher valuation.” The impact of a valuation cap can be intricate, influenced by factors such as whether the convertible note or SAFE is based on a pre-money or post-money basis, the number of investment rounds for these instruments, and the presence of an option pool or issued options under a stock option plan . An example will help illustrate how the valuation cap functions. Imagine that X Ventures invests $200, 000 into Startup Y, and they agree to a valuation cap of $2M with a SAFE. At the Series A round of financing, Startup Y raises an additional $1 million from VC Z. The only outstanding equity going into the Series A round are 900,000 common shares belonging to the founders of Startup Y. To simplify our discussion, let's focus on the impact of the valuation cap immediately after the SAFE converts and before the Series A round by VC Z. This will provide a clearer understanding of the effects of the valuation during this specific timeframe. Imagine the pre-money valuation ends up being $4M at the priced Series A round and no valuation cap existed for X Ventures. In this example, X Ventures investment of $200, 000 would result in 5% of equity ($200,000/$4,000,000) or 47,368 shares ((900,000 / 95%) – 900 000). However, let us bring the valuation cap into consideration. With a valuation cap of $2M, X Ventures will now receive 10% equity ($200,000 investment /$2,000,000) or 100,000 shares ((900,000 / 90%– 900 000)).  The valuation cap provides the investor with a premium of around 50, 000 shares! If that difference isn't shocking enough, let's now assume that the pre-money valuation was actually $20M at the priced Series A round. With the valuation cap, X Ventures still receives 10% equity or 100,000 shares. However, without the valuation cap, X Ventures would only receive 1% of the equity or 9,090 shares. The valuation cap provides X Ventures with 9% or nearly 100 000 more shares in Startup Y! Discount Rate A discount rate is another option that investors used to mitigate the risk of investing early into a startup. A discount rate is less complicated than a valuation cap. A discount rate is applied against the share price provided to equity investors in the financing in which the SAFE or Convertible Security converts (often times Series A ). A simple example will demonstrate the application of the discount rate. Suppose X Ventures invests $10,000 into a SAFE with a discount rate of 80% for Startup Y. Now assume that VC Z invests $10, 000 in a Startup during the Series A round where the shares are offered at $10/share. VC Z will receive 100 shares in the company ($10,000/ $10). X Ventures will receive 1, 250 shares ($10,000/0.8&$10). X Ventures receives 250 additional shares or 25% more equity than VC Z. Considering the advantages the valuation cap offers to the investor, it is advisable for the founder to explore negotiating a discount rate. Alternatively, maintaining the valuation cap at the highest possible level is also a strategic option. Conclusion Seed financing for tech-enabled startups is an important milestone, and the SAFE and convertible note represent the most common legal instruments used to raise capital at this stage. These instruments provide founders with a nuanced approach to securing capital, balancing the need for speed, cost-effectiveness, and flexibility. However, due to the effect of the valuation cap, founders must be very careful not to dilute their ownership stake when negotiation and agreeing to financing. Andrew Roy Legal helps tech-enabled startups with every stage of their journey from incorporation to IPO. Contact us today to learn more about how we can assist you. This article is intended for informational purposes only and should not be considered as legal advice and does not establish an attorney-client relationship. Consulting with a qualified legal professional is recommended for specific legal concerns and requirements related to your business. © 2023 Andrew Roy

Andrew Roy Legal assists tech-enabled startups throughout their journey from incorporation to IPO. Get in touch today to discover how we can support you. Welcome to Part 2 of our series, Legal Fundamentals for Canadian Technology Startups. We're here to offer a pragmatic, legal roadmap for launching your Canadian tech startup. Be sure to check out Part 1 Exploring Business Structures for Canadian Tech Startups: Sole Proprietorship and Partnership if you haven't already. What is a corporation? In Canada, the term "LLC" (Limited Liability Company), commonly used in the United States, does not apply. Instead, the typical equivalent in Canada is a corporation. Many Canadian tech startups begin as sole proprietorships or partnerships before evolving into corporations. A corporation is a separate legal entity, distinct from its owners and managers. It can hold property, operate businesses, and incur contractual and legal obligations, possessing all the rights, powers, and privileges of a natural person. Owners and managers of a corporation are shielded from its debts and other obligations thanks to its limited liability. Moreover, a corporation enjoys perpetual existence, meaning it continues indefinitely until dissolution either by its owners or by court order. Ownership of a corporation rests with its shareholders, which may include individuals, other corporations, partnerships, or trusts. Shares can be transferred between parties, subject to certain restrictions in specific circumstances. A corporation operates through its board of directors and officers. The directors, elected by shareholders, oversee the business and affairs of the corporation, appointing officers and delegating day-to-day management duties. Where should you incorporate your Canadian Tech Startup? In Canada, a corporation can be incorporated provincially or federally. Each province has its own legislation governing corporations, though these laws are more similar than not. The choice of where to incorporate depends on the jurisdiction where your corporation operates. In Alberta, for instance, one can opt for incorporation under the provincial Business Corporation’s Act RSA 2000, c B-9 (the "ABCA") or the federally regulated Canada Business Corporations Act (R.S.C., 1985, c. C-44) (the "CBCA") . Incorporation under the CBCA allows a corporation to establish its head office in any Canadian province and benefits from the perception that a federally regulated corporation enjoys greater international prestige. Moreover, the CBCA offers nationwide protection for the corporation's name. Incorporating under Alberta's ABCA brings its own benefits. For example, the Alberta Corporate Tax Act RSA 2000, c A-15 offers the country's lowest corporate tax rates to businesses incorporated in Alberta, allowing for more substantial tax benefits (to be detailed in a forthcoming article). Alberta also boasts a favorable corporate environment, with the ABCA not imposing residency requirements on corporate directors like the CBCA does. Furthermore, the ABCA doesn't necessitate that corporations maintain a register disclosing information about individuals with significant control or ownership, unlike the CBCA. For early-stage Canadian tech startups, incorporation under the ABCA is often more advantageous due to fewer regulations, reduced costs, and favorable tax treatment. Moreover, the ABCA is particularly beneficial for startups whose owners aren't Canadian residents. For these reasons, this article will focus on the incorporation process under the ABCA. How do you incorporate your Canadian Tech Startup? Incorporation in Alberta is achieved under the ABCA. Bear in mind that a corporation's existence is dependent on the governing statute. Section 5 of the ABCA states that one or more persons may incorporate a corporation by signing articles of incorporation and complying with Section 7 . Section 7 requires that an incorporator send to the Registrar the articles of incorporation and documents required in Sections 12(3) , 20 and 106 . Section 20 requires that a corporation have a registered office within Alberta and Section 106 requires the corporation send a notice of the directors. The Articles of incorporation are like a “birth certificate” for the corporation and contain valuable information about the corporation. Section 6(1) state that the articles of incorporation shall include the name of the corporation, the classes and maximum number of shares that the corporation is authorized to issue, whether there is a restriction to transfer shares of the corporation, the number of directors, including the minimum and maximum number of directors, and any restrictions on the businesses the corporation may carry on. Name The corporation's name must comply with Section 10 of the ABCA. One of the following terms should be included as part of the name: "Limited", "Limitée", "Incorporated", "Incorporée" or "Corporation", or the abbreviation "Ltd.", "Ltée", "Inc." or "Corp." placed at the end of the name. The corporation use that of another corporation, trust, association, partnership, sole proprietorship or individual in existence. A NUANS ("newly upgraded automated name search") report is required within 90 days before submitting the articles of incorporation to ensure no similar names exist. Remember, a corporate name differs from a trademark. Registering the name as a corporation under the ABCA doesn't offer the same protections as a registered trademark under the Trademarks Act. See our series on trademarks on the process for registering a trademark. Registered Office The corporation's registered office address, where it can receive legal documents and maintain corporate records, must be included in the articles. Per Section 20(1) , a post office box cannot serve as the registered office address. Share Capital As stipulated by Section 26(1) , every corporation is mandated to have at least one class of shares. The articles define the number and class of shares a corporation is authorized to issue. Three rights are typically embedded in one or more classes of shares: the right to vote at shareholder meetings, the right to receive dividends as declared by the board of directors, and the right to partake in the residual value of the corporation’s assets upon its liquidation. If a corporation has more than one class of shares, the typical share classes include: Common shares: This class includes the three rights previously discussed. Non-voting common shares: These shares hold the same rights, privileges, restrictions, and conditions as common shares, excluding the right to vote. Preferred or special shares: These shares have a fixed liquidation preference and must be paid before any common shares. They may also include other rights attached to common shares. Frequently, incorporators introduce a class of "blank cheque" preferred shares. These are authorized but unissued preferred shares that can be issued in series. The rights, privileges, restrictions, and conditions of these shares can be determined by a corporation's board of directors per Section 29 of the ABCA. "Blank cheque" preferred shares can be used to raise additional funds or as an anti-takeover defense by enabling the board to create a new series of preferred shares with specific voting, conversion, or control rights. The exact share structure depends on various factors like financing needs and strategies, control of the corporation, investor expectations, exit strategy, tax issues, and possible employee incentive programs. A qualified lawyer's advice on structuring your share capital is crucial to your business success. Restrictions on Share Transfers The articles also outline any restrictions on the issue, transfer, or ownership of shares. Transfer restrictions are essential for various reasons, including controlling the business ownership and leveraging commonly used prospectus exemptions. More on share issuances and transfers will be covered in a subsequent article. Directors The articles must state the full legal name and address of the first director(s) and specify whether there is a fixed number of directors or a range (minimum and maximum number). If a range is chosen, the actual number of directors at any given time must fall within that range. Every corporation must have at least one director. Restrictions on the Business of the Corporation Restrictions on a corporation's business are common in professional corporations for doctors or lawyers but are not frequent in Canadian legal tech startups. Filing the Articles of Incorporation Filing an incorporation can be done through a registry agent or authorized service provider and must include: Articles of Incorporation Notice of English/French Name Equivalency (optional) Notice of Address Notice of Directors Notice of Agent for Service for an Alberta or Extra-Provincial Corporation NUANS report Valid ID Fee payment Once the Director under the ABCA issues a certificate of incorporation, the corporation is officially incorporated as of the date indicated on the certificate. Directors and Officers Directors manage the overall business and affairs of the corporation, and the board appoints officers to handle day-to-day matters. A private corporation must have at least one director. According to Section 105(1) of the ABCA, a director must be at least 18 years of age, must not be bankrupt, must be an individual, and must be of sound mind pursuant to various legislations. Unlike the CBCA, the ABCA does not require director(s) to be Alberta residents. Individuals can become a director of a corporation through the articles of incorporation, election by shareholders, or appointment by the board of directors to fill a vacancy. A director may resign, become disqualified, or be removed from office by the shareholders. Officers manage the daily operations of a corporation and can be appointed by the directors. Directors and officers may be subject to personal liability due to their roles within the corporation. According to Section 122(1) of the ABCA, directors and officers have a duty to act honestly and in good faith, with the corporation's best interests in mind, and must exercise care, diligence, and skill. In addition, different Canadian statutes impose personal liability for: Unpaid employee wages and vacation pay Failure to remit employee income tax, employment insurance, and Canada pension plan source deductions Failure to collect sales tax Offenses under environmental protection legislation Payment of dividends if the corporation is insolvent Misrepresentation in certain public company disclosure documents After Incorporation of your Canadian Tech Startup Once the articles of incorporation are filed and a certificate issued, the corporation must be organized, and directors must prepare certain corporate records. Bylaws are regulations made by the directors governing the corporation's internal affairs, addressing matters not included in the articles or corporate statute. Bylaws generally outline the identification process for contract signatories, the protocol for calling directors’ meetings, the quorum for these meetings, and the rules for shareholder meetings, among other things. These bylaws, once adopted by directors, must be confirmed at the next shareholders' meeting to remain in effect. Directors can modify the bylaws subject to shareholder ratification. Pursuant to 105(5) of the ABCA, a person is a director when elected or appointed at a meeting and does not refuse to act as a director, or when not present at the meeting, consents to act as a director before the meeting or within ten days after the meeting. Most corporations keep certain corporate records in a minute book,. The minute book can be understood as the “photo album” or the "family history" of the corporation and includes the articles of incorporation and any amendments bylaws shareholder agreements minutes of directors and shareholder meetings resolutions of directors and officers registers of directors, officers, securities, and transfers of securities share ledgers, and debt obligations. Tax Perspective Corporate tax is a distinct discipline, and this article can only provide a brief overview of the topic. Because the corporation is a separate legal entity, it pays its tax separately from shareholders. A shareholder cannot include the corporation’s income or loss in their personal income. When the corporation's income is distributed to shareholders, it must be in the form of a dividend from the corporation's after-tax income. Dividends to individual shareholders constitute taxable income, even though the corporation has already paid tax on them. Integration mechanisms (such as the gross-up and dividend tax credit or refundable corporate taxes) help eliminate or reduce the element of double-taxation that would result from the imposition of tax at both the corporate and shareholder levels. When a shareholder sells their shares, it's generally taxed as a capital gain. Under certain conditions, this gain may be tax-exempt up to a limit. Alberta’s corporate tax rates, the lowest in Canada, stand at 8%, plus the federal rate of 15%, equating to a combined rate of 23% . There are further tax deductions available to Canadian Controlled Private Corporations (the “CCPC”) on the first $500,000 of active business income and additional tax advantages like the Scientific Research and Experimental Development Program (the "SR&ED") (a future article will consider the CCPC and SR&ED in depth). Corporations also have the advantage of compensating employees and independent contractors with equity, an important tool for Canadian tech startups to attract, incentivize, and retain key personnel. There are many forms of equity-based compensation available to corporations, including stock options, restricted share units, deferred share units, and stock issuances. Future articles will explore the tax consequences arising from these types of equity-based compensation. Conclusion Navigating the world of incorporation in Alberta may appear complex, but it can offer many benefits for Canadian tech startups, especially given Alberta's favourable corporate tax rates and flexible regulatory environment. It's essential to understand the responsibilities that come with incorporating, such as establishing a registered office, formulating a share structure, adhering to the stipulations concerning directors and officers, and maintaining comprehensive corporate records. Also crucial is an understanding of the tax implications for both the corporation and shareholders. While this article has aimed to provide a broad overview, it is always wise to engage a legal professional when incorporating a business to ensure all legal and financial aspects are properly addressed, thus laying a solid foundation for the future success of your tech startup. Stay tuned for more in-depth articles on corporate tax deductions, equity-based compensation, and other relevant topics for your Canadian tech startups. At Andrew Roy Legal, we provide a range of advice relating to corporate and commercial law. We offer flat fees to provide you and your business with certainty. Contact us today to learn more about how we can assist you. This article is intended for informational purposes only and should not be considered as legal advice and does not establish an attorney-client relationship. Consulting with a qualified legal professional is recommended for specific legal concerns and requirements related to your business. © 2023 Andrew Roy

Andrew Roy Legal assists tech-enabled startups throughout their journey from incorporation to IPO. Get in touch today to discover how we can support you. Introduction In 2022, the House of Commons Committee in Canada embarked on a groundbreaking study to explore the vast potential of blockchain technology across various sectors, including on Canadian crypto law. The study aimed to assess the current status of blockchain, crypto adoption, and crypto law in the country, engaging in extensive consultations, gathering insights from 31 expert witnesses, and analyzing six briefs. The findings shed light on the manifold benefits and challenges associated with blockchain technology and crypto law, with particular emphasis on the need for robust regulatory frameworks. In this comprehensive analysis, we delve into the implications of the study's revelations, with a focus on the legal aspects surrounding cryptocurrencies and blockchain technology in Canada. Blockchain Technology and Its Evolution Originating from Satoshi Nakamoto's seminal 2008 paper , blockchain technology emerged as a distributed ledger system. It operates on a network where every node possesses a copy of the ledger and validates information through consensus mechanisms. The addition of encrypted data blocks ensures data integrity and eliminates retroactive alterations, all without the need for a central authority. Blockchain networks can be open/permissionless (e.g., Bitcoin, Ethereum) or private/permissioned, with restricted access to certain nodes. Although often confused with blockchain technology, cryptocurrencies represent digital currencies that utilize blockchain as their underlying infrastructure. Cryptocurrencies and Their Applications Blockchain technology and cryptocurrencies are intrinsically linked, as blockchain serves as a digital ledger enabling secure electronic transfers and storage of value. Modeled after Bitcoin's blockchain, most cryptocurrencies facilitate transactions and validate the ledger using cryptographic keys to ensure secure ownership verification. While Bitcoin uses a proof-of-work consensus mechanism, where nodes compete to validate transactions through computational problems, Ethereum has transitioned to a proof-of-stake model. Together, blockchain and cryptocurrencies form the core of Web 3, the third phase of the internet, empowering individuals with ownership of digital assets and online privacy. Characteristics and Challenges of Blockchain Technology Blockchain networks boast four key characteristics : transparency, traceability, immutability, and disintermediation, allowing for peer-to-peer transactions without intermediary involvement. These features provide data decentralization, enhanced security, and proven business solutions. However, blockchain technology faces several challenges, including concerns over energy consumption, limited scalability, high transaction fees, lack of standardized regulations, and the ongoing development of regulatory frameworks. Diverse Applications of Blockchain Technology Blockchain's applications can be broadly categorized into two groups: t hose related to cryptocurrencies and those in various sectors beyond finance. Expert witnesses cautioned against assuming that the most valuable applications of blockchain technology are already known, likening the early days of blockchain to the nascent stages of the internet. Cryptocurrencies, created using blockchain technology, include cryptocurrencies themselves, cyber-indexed tokens (e.g., NFTs ), and utility tokens serving specific purposes. Beyond cryptocurrencies, blockchain technology finds application in diverse non-cryptocurrency fields, such as efficient and transparent supply chains, pharmaceutical provenance, food security, tracking CO2 emissions, and improving carbon credit systems. Blockchain also enables automated revenue distribution in cultural industries and enhances fraud detection in music royalty distribution. The advent of non-fungible tokens (NFTs) has created entirely new digital-native industries centered around unique asset ownership. Financial Applications and Canada's Role in the Blockchain Industry The financial sector significantly benefits from blockchain technology, with cryptocurrencies serving as viable investment opportunities and "smart contracts" driving the growth of decentralized finance ( DeFi ). Blockchain enables fractionalized ownership of assets like real estate, fosters financial inclusion for underserved populations, and facilitates quicker cross-border transactions with reduced settlement times. Canada stands as an innovative leader in the blockchain industry, with substantial contributions to projects like Ethereum. However, expert witnesses warned of potential talent and entrepreneurship loss due to the lack of regulatory clarity and support, which might impede the country's progress in this rapidly evolving field. Economic Impact and Regulatory Landscape of Crypto Law in Canada Blockchain's economic impact in Canada is evident, with billions invested in the sector. While the number of businesses adopting blockchain technology remains relatively low, it is steadily increasing, providing employment to around 16,000 individuals and offering competitive salaries. Despite its potential, the industry faces regulatory challenges at both federal and provincial levels. Regulators focus on cryptocurrency trading platforms and related financial services to safeguard consumers and combat money laundering concerns. The taxation of cryptocurrencies involves considering them as commodities, resulting in capital gains or losses. Provincial securities regulators view many crypto assets as securities subject to securities legislation. The regulatory sandbox program offers temporary relief to firms to comply with regulations. Canada's Approach to Regulating Cryptocurrency Trading Platforms Canada hosts approximately 11 Canadian-based cryptocurrency trading platforms catering to Canadians and at least 15 foreign platforms serving the country. In the aftermath of the collapse of QuadrigaCX in 2019, Canadian regulators adopted a more robust approach to regulating these platforms to protect consumers. Witness testimonies highlighted the stringent regulatory requirements that platforms must adhere to, including the implementation of cold storage for client assets. Some platforms are registered with Canadian securities regulators and the Financial Transactions and Reports Analysis Centre of Canada (FINTRAC), with robust anti-money laundering and compliance programs. Cryptocurrency custodians play a crucial role in risk mitigation and securing assets for institutions and investors. Canada's Competitive Advantage in Cryptocurrency Mining Canada boasts a competitive advantage in cryptocurrency mining, driven by factors such as the rule of law, infrastructure, a well-educated workforce, natural cooling systems, and access to clean, renewable energy. Mining operations provide significant value to the Canadian economy and contribute to smaller population centers. However, concerns arise over high energy consumption, comparable to that of entire countries, leading some utilities to restrict new connections. Witnesses highlighted the need for innovations in renewable energy sources and alternative consensus mechanisms to mitigate environmental impacts and support the power grid. Overcoming Challenges and Embracing Opportunities While the blockchain industry has witnessed remarkable growth and potential, it has also faced significant challenges, with high-profile fraud cases like FTX . Expert witnesses emphasized the importance of differentiating technology from bad actors and the need for effective regulations to protect consumers from potential risks associated with cryptocurrencies. Nonetheless, the majority of experts remain optimistic about the transformative potential of blockchain technology. Promoting Regulatory Clarity and Government Support of Crypto Law To foster the growth of the blockchain industry in Canada, regulatory clarity is paramount. A lack of standardized regulations and jurisdictional oversight has led some firms to relocate to other countries, affecting talent retention and consumer protection. Expert witnesses recommended the development of a digital asset taxonomy and differentiated regulations, with the European Union's (EU) comprehensive MiCA regulations cited as a potential model. To address the lack of regulatory clarity, witnesses called for increased government engagement with the blockchain industry and the establishment of a national strategy. Stablecoins received particular attention, with recommendations for a comprehensive regulatory regime. Witnesses also highlighted the potential of Central Bank Digital Currencies (CBDCs) and their benefits in blockchain-based transactions. Furthermore, witnesses emphasized the importance of government support in research, commercialization, and education within the blockchain industry. They proposed targeted investments to bolster research and commercialization efforts, drawing parallels with the government's successful support of artificial intelligence research. Education was identified as a critical aspect to enhance public understanding of blockchain technology and protect consumers from fraud. Blockchain education, beginning at the elementary school level, was considered essential to nurture talent and create a competitive workforce within the global industry. List of Recommendations In response to the information gathered by the Government of Canada, they released the following sixteen recommendations: That the Government of Canada recognize blockchain as an emerging industry in Canada, with significant long-term economic and job creation opportunities. That the government of Canada should, in its efforts to improve consumer protection and regulatory clarity to the emerging and innovative field of digital assets, be guided by the principle that individuals’ right to self custody should be protected and that ease of access to safe and reliable on and off ramps should be defended and promoted. That the Government of Canada, following consultation with the provinces and stakeholders, establish a national blockchain strategy that clarifies the government’s policy direction and regulatory approach, and demonstrates support for the industry. That the Government of Canada, with a view to adopting a national blockchain and distributed ledger strategy call on a group of experts, entrepreneurs, academics and investors, as well as people in the artificial intelligence industry cluster, to support its analysis and understanding and help it determine best steps; and give the group a mandate to set up a platform for information exchange and monitoring; carry out analyses to identify the most promising or high-risk areas for disruption; advise the government on promising initiatives; and support the government in implementing selected initiatives. That the Government of Canada pursue opportunities for international cooperation in the development of blockchain regulations and policies, including with our major trading partners. That the Government of Canada conduct innovative pilot projects using distributed ledgers to help strengthen the ecosystem and recognize up-and-coming businesses. That the Government of Canada create a sandbox where entrepreneurs can test technologies unhindered by as yet unadopted regulations. That the Government of Canada adopt a distinct regulatory approach to stablecoins that reflects the difference between these products and other cryptocurrencies, and account for the unique regulatory challenges they present. That the Government of Canada adopt regulatory changes to promote the establishment of federally regulated cryptocurrency custodians to meet the demand for cold storage services from Canadian cryptocurrency firms. That the Government of Canada adopt measures for access to banking and insurance services for blockchain firms, including through Crown corporations. That the Government of Canada establish a public awareness campaign, in consultation with the provinces and the industry, to educate the public about risks related to cryptocurrencies and the benefits of accessing cryptocurrency markets through regulated Canadian entities. That the Government of Canada draw on the previous report on SMEs and launch a strategic initiative to develop skills and talent and support research. That the Government of Canada investigate ways to promote the adoption of blockchain technology in supply chains. That the Government of Canada, in collaboration with the Commissioner of Canada Elections, undertake a study on the new opportunities this technology presents for electronic voting, consultation, and the modernization of our democratic institutions. That the government of Canada should investigate equity between provinces in the application of the Excise Tax Act to mining activities to ensure fair taxation. That the government of Canada, in order to foster a competitive digital asset mining environment and in order to continue to attract investments, should maintain that digital asset mining constitutes a commercial activity in Canada; and as such adopt a neutral and equitable position towards this new and growing industry. Conclusion The findings from the House of Commons Committee's study demonstrate that blockchain technology and cryptocurrencies possess immense potential to revolutionize various sectors and boost economic growth in Canada. Despite facing challenges such as regulatory clarity, high energy consumption, and concerns over criminal activity, the blockchain industry in Canada continues to thrive. With recommendations for regulatory clarity, government support, and strategic initiatives, the country can become a global leader in this transformative technology, unleashing countless opportunities for innovation and prosperity in the years to come.

We provide advice on all things related to blockchain and cryptocurrencies . We offer flat fees to provide you and your business with certainty. Contact us today to learn more about how we can assist you. This article is intended for informational purposes only and should not be considered as legal advice and does not establish an attorney-client relationship. Consulting with a qualified legal professional is recommended for specific legal concerns and requirements related to your business. © 2023 Andrew Roy

This is Part 1 of our series on Legal Fundamentals for Technology Startups, where we provide practical resources for common legal questions for technology startups. Sign up to have the articles delivered directly to your email: When embarking on a new startup, one of the primary considerations is the type of business structure to adopt. This decision holds significant implications, including taxation, liability, and the ability to raise capital. In this article, we will explore the options of sole proprietorship, partnerships, and joint ventures. Sole Proprietorship for Canadian Tech Startups For many entrepreneurs of Canadian tech startups, especially those seeking simplicity and low setup costs, a sole proprietorship may be an attractive option. Often times, startups will begin with a sole proprietorship and when the business begins to generate positive cash flow, transition to a more complicated business structure. A sole proprietorship allows the owner, known as the sole proprietor, to retain complete control over the business and, in this structure, all income, assets, and obligations of the business belong to the sole proprietor. A sole proprietorship can only have one owner but can employ staff, and the sole proprietor owns all business assets and maintains full control over business operations and decision-making. Unless the business operates under a different name, there are no registration or constitutional document requirements. As many Canadian tech startups will take a few years to become profitable and are often created as a "side gig," this structure allows the sole proprietor to keep costs low while offsetting business losses against other income. However, because this business structure does not have a distinct legal identity and cannot own assets or grant security in its name, the sole proprietor has unlimited financial liability and is personally responsible for all financial obligations, including potential risks to personal assets. As a result, it is often recommended that the owner transition from this structure as it grows in order to limit the personal liability of the owner. Partnerships for Canadian Tech Startups Partnerships are another vehicle available to structure a business for Canadian tech startups. The two most common partnership structures for startups are general partnerships and limited partnerships. General Partnership A general partnership allows two or more individuals (or corporations) to work together and share profits. Unlike other business structures like a corporation or limited partnership, general partnerships do not require formal documents or complex registration procedures. A general partnership can be created with a simple verbal agreement, although it is recommended to have a written agreement. A general partnership would make it easier for multiple entrepreneurs to pool their resources and raise capital for the business. This option for Canadian tech startups is often used when two friends or business partners start up a business together, but do not yet have the capital or cash flow to create a more complicated business structure. To establish a general partnership, all that is needed is a registered trade name, a tax number for tax payments, and a bank account. Profits and losses in a general partnership are passed through to the individual partners, who report them on their personal tax returns. Additionally, dissolving a general partnership is as straightforward as establishing one, with no additional legal obligations beyond reporting the business closure on tax returns. On the other hand, all partners have unlimited joint liability, and general partnerships face challenges in attracting external investments due to limited credibility and potential risks associated with unlimited liability. One partner's decisions on behalf of the business are binding for all partners, potentially leading to shared responsibility for contracts, assets, and debts incurred by the partnership. Personal assets of individual partners may be seized to cover damages or unpaid business debts. It is crucial to choose business partners wisely and establish a partnership agreement to clarify management accountability, ownership, and profit distribution. General partnerships are generally unsuitable for passive investors and are not used to raise equity. As a result, it is generally recommended that once a Canadian tech startup wishes to raise capital, it does not use a general partnership. Limited Partnership A limited partnership can be formed to carry on any business that a general partnership may carry on. The main feature of a limited partnership is that the liability of each limited partner is limited to the amount of capital such limited partner contributed to the partnership. This option could be used for a Canadian tech startup if a general partnership attracts a silent investor, and the owners do not want to deal with the formalities of a corporation. Limited partnerships raise capital through the issuance of equity (partnership interests) and the incurrence of debt. Partnership interests are typically issued in private placements. Limited partners are prohibited from managing the partnership, making it a suitable vehicle for raising capital with silent investors. However, in order to protect the liability of the general partners, corporations are commonly used as general partners of a limited partnership. Forming a limited partnership under the Alberta Partnership Act (APA) is advantageous in specific circumstances. It is preferred when most partners reside in Alberta, the firm has Alberta counsel, and the business operations are limited to Alberta. If a partnership is formed in another province or territory and conducts business in Alberta, it must register as an extra-provincial limited partnership. To form and organize a limited partnership in Alberta, a limited partnership agreement must be drafted, which is a private document outlining the rights and obligations of the partners and overriding default provisions of the APA. A limited partnership must have at least two partners, including one general partner and one limited partner. A person can be both a general partner and a limited partner simultaneously, as long as there is at least one other general partner or limited partner (that is, the requirement for at least two partners at all times is met). A certificate establishing the limited partnership must be recorded with the Registrar of Corporations. It includes the firm name, names, email addresses, and addresses of each general partner, and other required information. General partners have broad authority to contract on behalf of the firm, and the firm is generally bound by contracts made by the general partners acting within their authority. However, there are limitations on a general partner's authority as specified in the APA. These limitations include acts contrary to the agreement between partners, actions hindering the firm's ordinary business, and other restrictions set out in the agreement. There are no registration renewal or annual filing requirements for limited partnerships under the APA. However, if there are changes in the information stated in the certificate, the partnership must deliver a notice to amend the certificate to the Registrar. A limited partner still has certain liabilities that he or she needs to be aware of. Limited partners may become liable for the firm's debts and obligations if the limited partner fails to make their full capital contribution as stated in the limited partnership agreement. he or she becomes liable to the firm to the extent of the agreed capital contribution. Additionally, if a limited partner receives a return of their capital contribution but the firm has unpaid creditors, the limited partner is liable to the extent of the amount returned, including interest, necessary to satisfy the firm's liability to the creditors. If a limited partner's surname appears in the firm's name, they are liable as a general partner to any creditor who extended credit to the firm without actual knowledge that the limited partner is not a general partner. If a limited partner participates in controlling the firm's business, they become liable for its debts and obligations. Additional Structures: Unlimited Liability Company, Limited Liability Partnership, and Joint Venture The unlimited liability company, the limited liability partnership, and joint ventures are additional structures for businesses, but these structures are much less common for Canadian tech startups. Therefore, we will only provide a short overview of each. Unlimited Liability Company An unlimited liability company is a unique form of business entity in Canada, which can only be incorporated under the provincial laws of Alberta, British Columbia, and Nova Scotia. In an unlimited liability company, shareholders are personally liable for the liabilities of the company. They are generally used by foreign investors to gain advantageous tax treatments in their home jurisdiction. This occurs because, while a unlimited liability company is taxed as a corporation in Canada, they may be eligible for a "check-the-box" election in the United States, permitting them to be taxed as a flow-through entity. Limited Liability Partnership: A limited liability partnership may be formed under Alberta law only for the purpose of practicing one or more eligible professions governed by an Act of the Alberta Legislature (section 82(1) of the Partnership Act ). An eligible profession is a profession or discipline regulated by an Act of the Alberta Legislature that specifically authorizes members of the profession or discipline to carry on business through a corporation that has the words "Professional Corporation" or the abbreviation "P.C." as part of its name (section 81, Partnership Act ). Joint Venture Two or more parties may engage in a joint venture where they collaborate on a business venture. There is no specific statutory definition or regulatory scheme for joint ventures in Canada, at either the provincial or federal level. Private equity investors often enter joint ventures with managers or operators, and they are common in the healthcare and infrastructure industries. Cross-border joint ventures are also common because often US private equity firms will invest in Canadian companies, and the Canadian management teams or operators will maintain a stake in the businesses. Joint ventures can be contractual (two or more entities agreeing to combine resources or expertise to carry out a specific project or venture) or corporate (a corporate joint venture arises where two or more entities decide to use a corporation as the joint venture vehicle and become shareholders of the corporation). Conclusion The sole proprietorship, the general partnership, and the limited partnerships are common business structures for Canadian tech startups. The central disadvantage of the sole proprietorship and the general partnership is the difficulty in raising capital due to the unlimited liability of the owners or partners. These structures are generally advised when the business is first starting and the entrepreneurs can offset their losses against other income. A limited liability partnership can be a way for passive investment and raising funds without having to comply with the more formal requirements of a corporation. However, normally once a startup starts turning a profit and has sufficient cash flow, they create a corporate structure to limit their personal liability. In the next post of this series, we will discuss the corporate body. We help Canadian tech startups structure their business. We offer flat fees to provide you and your business with certainty at this early stage of the process. Contact us today to learn more about how we can assist you. This article is intended for informational purposes only and should not be considered as legal advice and does not establish an attorney-client relationship. Consulting with a qualified legal professional is recommended for specific legal concerns and requirements related to your business. © 2023 Andrew Roy

Breaking Barriers: Court of King's Bench Pioneers the Tort of Harassment in Alberta's Landmark Ruling Introduction Justice Colin Feasby (Feasby J.) of the Court of King’s Bench of Alberta made a significant legal development by establishing a new tort of harassment in the province of Alberta. This groundbreaking decision was reached in the case of Alberta Health Services v Johnston, 2023 ABKB 209 , which emerged from the highly publicized harassment inflicted by former mayoral candidate Kevin Johnston upon Alberta Health Services (AHS) employees during the peak of the COVID-19 pandemic. Justice Feasby's ruling introduces a tort of harassment, marking a crucial milestone in Canadian law. An Emerging Tort of Harassment Feasby J. first considers the discussion and controversy of an emerging tort of harassment in Ontario and BC. The Ontario Court of Appeal in the case of Merrifield v. Canada (Attorney General), 2019 ONCA 205 ( Merrifield ) concluded that there is no existing tort of harassment in Ontario and that the development of such a tort would require compelling reasons. Similarly, the BC Courts have not recognized the tort of harassment in BC. However, despite the hesitation expressed in Merrifield , several decisions from the Ontario Superior Court and other jurisdictions have recognized a narrower tort of internet harassment. This recognition of this narrower tort raises questions about the inconsistency of recognizing a tort solely based on the medium used, while denying a tort for the larger context. Feasby J. then considers Justice Graesser (Graesser J.) decision in the in the case of Ford v Jivraj, 2023 ABKB 92 ( Ford ), who expressed disagreement with the Ontario Court of Appeal's decision in Merrifield and saw harassment as a logical extension of the existing tort of intentional infliction of mental suffering. While Feasby J. agrees with Graesser J. that Alberta courts are not bound by the Ontario Court of Appeal, he disagrees that the tort of harassment is simply an extension of intentional infliction of mental suffering. Law of Recognizing New Torts Feasby J. discusses the law and process for recognizing new torts in Canadian law. According to the Supreme Court of Canada in Nevsun Resources Ltd. v. Araya, 2020 SCC 5 (CanLII), [2020] 1 SCR 166 ( Nevsun ), the development of the common law occurs when it is necessary to clarify a legal principle, resolve inconsistencies, or keep the law aligned with societal evolution. The judges have the authority to extend existing principles or apply existing remedies to protect rights that are not adequately addressed. A key consideration in recognizing a new tort is whether the harm in question cannot be adequately addressed by existing recognized torts. The majority opinion in Nevsun emphasizes the need for the new tort to address a specific wrong that lacks sufficient remedies. However, the dissenting opinion takes a more restrictive view, stating that courts will not recognize a new tort when there are adequate alternative remedies, where the tort does not reflect and address a wrong visited by one person upon another, and where the change wrought upon the legal system would be indeterminate or substantial. For a proposed nominate tort to be recognized by the courts, at a minimum it must reflect a wrong, be necessary to address that wrong, and be an appropriate subject of judicial consideration. Feasby J. also highlights the historical context of the existing torts, which were developed over centuries without adequately recognizing the harms experienced by women and marginalized groups. The author acknowledges that harassment disproportionately affects these groups, and the failure to recognize a tort of harassment in the past does not mean it should not exist. Justifying Recognition of a Tort of Harassment Feasby J. then provides his reasoning for why the courts should recognize a tort of harassment: First, harassment is a criminal offense, and the fact that harassment is a crime suggests that it is reasonable to ask whether it is also something for which a civil remedy should exist. Second, he states that while the Alberta legislature has this ability to create a statutory cause of action, the development of common law provides justices with this right to create new rights of actions. Third, the Court of King’s Bench routinely provides restraining order applications for harassment. While the doctrinal basis for granting restraining orders in cases of harassment is unclear, various legal justifications have been suggested, such as protecting the freedom of the applicant from harassing conduct or addressing vexatious behavior. The power to grant restraining orders stems from the Court's inherent jurisdiction, allowing for the issuance of remedies to protect legal or equitable claims. Recognition of the tort of harassment would enable the court to award damages in addition to issuing restraining orders, offering a more comprehensive response to the problem of harassment. Fourth, he finds that existing torts fail to adequately address the harm caused by harassment. While defamation and assault touch on certain aspects of harassment, they are limited in scope, addressing false statements causing reputational harm and imminent threats of physical harm, respectively. The new privacy torts only apply when there is a reasonable expectation of privacy, which may not be present in cases of harassment. The tort of private nuisance, used in the context of repeated telephone calls, is inadequate for many harassment situations as it requires a connection to property. The tort of intimidation also has limited applicability as it necessitates submission to a threat, whereas harassment often lacks a clear threat or submission. Where Graesser J in Ford and other scholars propose viewing harassment as an extension of the tort of intentional infliction of mental suffering, Feasby J finds that this particular tort requires flagrant or outrageous conduct calculated to produce harm, resulting in a visible and provable illness. Harassers often act recklessly rather than with clear intention, and victims may resort to self-preservation behaviors that don't lead to visible or provable illness. Consequently, the harms and costs associated with harassment that fall short of a visible or provable illness cannot be recovered under the tort of intentional infliction of mental suffering. The Elements of the Tort of Harassment After canvassing the reasons for establishing a tort of harassment, Feasby J finds that the new tort of harassment requires a four-part test. The tort is made out where a defendant has: Engaged in repeated communications, threats, insults, stalking or other harassing behavior in person or through other means. That he knew or ought to have known was unwelcome. Which impugn the dignity of the plaintiff, would cause a reasonable person to fear for the plaintiff's safety or the safety of the plaintiff's loved ones, or could foreseeably cause emotional distress, and Caused harm. Remedies Feasby J. then discusses the damages or remedies for this new tort, where he draws from defamation. General damages in defamation cases are presumed without the need to prove actual injury. Factors for assessing general damages include the plaintiff's position and standing, the nature and seriousness of the defamatory statements, the mode and extent of publication, the absence of retraction or apology, and the conduct and motive of the defendant. In this case, the plaintiff, Ms. Nunn, was a public health inspector who faced attacks on her professionalism and was labeled a criminal and a terrorist. The attacks were widely disseminated through mainstream media and online platforms. The defendant, Mr. Johnston, did not apologize and maintained his position. Drawing from Hill v. Church of Scientology of Toronto, 1995 CanLII 59 (SCC), [1995] 2 SCR 1130 , the court awarded: General damages of $300,000 for injury to her reputation, considering the significant public interest in the COVID-19 pandemic; $100,000 in general damages for the harassment she experienced, which caused her fear and negatively impacted her quality of life; Aggravated damages were awarded in the amount of $250,000. Permanent injunctions restraining Mr. Johnston’s activities in relation to AHS and Ms. Nunn; Costs multiplied by three. Conclusion Feasby J. establishment of the new tort of harassment in the province of Alberta marks a significant legal development and will have wide ranging impact on a variety of contexts in the civil litigation sphere. Accusations of harassment are common in employment, business, and domestic contexts, and this tort, including the significant amount of damages awarded, represents a significant risk to business and employers, while providing another right of action by those impacted by serious and continual harassing behaviour. Andrew Roy Legal offers a variety of civil litigation services. Contact us today to learn more about how we can assist you. The information in this article is not legal advice and does not establish an attorney-client relationship. © 2023 Andrew Roy

Navigating Trademark Oppositions: What You Need to Know If you enjoy this content, please consider signing up . Creating a member account is free, and you will · receive new content delivered directly to your inbox; · have exclusive access to members only content; · gain access to our online booking tool; · collect 500 bonus points in our points program. Introduction Picture this: you've registered a trademark, and you're going about your business when suddenly, a letter from a law firm arrives. It informs you that you must either abandon your trademark or prepare for an opposition hearing. Confusion and concern set in. What exactly is a trademark opposition statement? In this blog post, we'll break down the process of a trademark opposition and provide you a better understanding of what lies ahead. Statement of Opposition If someone wants to halt the registration process of an application, they must file a statement of opposition with the Registrar (the "Opposition"). Per Section 38(1) of the Trademarks Act , RSC 1985, c T-13 (the “ Act ”) , to file an Opposition, a party has two months from the date of a trademark advertisement. The Opponent is the person that wishes to oppose the trademark (the "Opponent"), and the applicant is the one who originally submitted the application for the trademark (the "Applicant"). An Opponent can oppose the registration of a trademark regardless of whether it directly affects their businesses. They only need to allege that the mark is unregistrable. Per Section 38(3) of the Act , the Opposition should include detailed grounds for the Opposition and the Opponent's address. Section 38(2) provides the grounds on which an Opposition may be based: (a) that the application does not conform to the requirements of subsection 30(2) , without taking into account if it meets the requirement in subsection 30(3) ; (a.1) that the application was filed in bad faith; (b) that the trademark is not registrable ; (c) that the applicant is not the person entitled to registration of the trademark; (d) that the trademark is not distinctive ; (e) that, at the filing date of the application in Canada, determined without taking into account subsection 34(1) , the applicant was not using and did not propose to use the trademark in Canada in association with the goods or services specified in the application; or (f) that, at the filing date of the application in Canada, determined without taking into account subsection 34(1) , the applicant was not entitled to use the trademark in Canada in association with those goods or services. The date for assessing the Applicant's compliance with the above-mentioned grounds depends on the particular section (i.e. the “material date”) (it is important to contact a trademark agent or lawyer to find out the material date for each particular section, as they can differ). In an Opposition, the Opponent must include the material facts that the support the opposition to the Applicant's trademark. The Opposition Board may reject an Opposition that lacks supporting facts. If an Opposition lacks sufficient detail, the Applicant can request an amended statement, but the Board only checks if one substantial issue is raised. The Board cannot consider a ground not raised by the Opponent. The Opposition Board generally only considers issues raised in the original Opposition. If the Opponent wants to raise additional issues, they must seek permission to amend the Opposition. The Registrar has discretion to grant or refuse an amendment based on legal principles and the interests of justice. The Registrar can reject an Opposition that does not raise a substantial issue or is frivolous. A substantial issue is one that could potentially lead to success for the Opponent if it is proven. The Registrar must assume the truth of the allegations in the Opposition and cannot apply a higher test of likelihood of success. Counter Statement by Applicant If the Registrar determines that the Opponent has raised at least one substantial issue in the an Opposition, they will forward it to the Applicant, who can response (it is noted that this is generally the time when the Applicant will call the lawyer). The Applicant, within one month of receiving the statement, must file a counterstatement with the Trade-marks Office and serve a copy on the Opponent. The counterstatement and any additional material must be served personally or by registered mail to the Opponent's address in Canada, with service deemed effective on the mailing date. The Applicant must notify the Registrar once service has been affected and, in his correspondence with the Registrar, the Applicant must clearly indicate (1) manner of service and (2) the date of service. If the Applicant does not file a counterstatement within the one-month time limit that is prescribed, its application for registration of a trade-mark will be deemed to have been abandoned. Evidence for Opposition Proceeding In the opposition proceeding, both the Applicant and the Opponent can submit evidence to the Registrar through affidavits or statutory declarations. The Opponent must submit evidence within one month of receiving the Applicant's counterstatement, while the Applicant must submit evidence within one month of receiving the Opponent's evidence. Failure to do so may result in the Opposition being deemed withdrawn or the application being deemed abandoned. Exhibits should be filed along with the affidavits or declarations. The rules of evidence applicable in the Federal Court generally apply (it is important to contact a trademark agent or lawyer to understand the nuances of these rules). If a party fails to provide relevant evidence within their knowledge, the Registrar may draw a negative inference from that absence. Parties can cross-examine each other on this evidence, but the party conducting the cross-examination must file a transcript of the cross-examination, exhibits, and any submitted documents within the designated timeframe. The party requesting the cross-examination is responsible for meeting the timelines set by the Registrar. If a party delays in fulfilling undertakings, the Opposition Board can set a deadline. Written and Oral Arguments Following the exchange of the evidence and the cross-examination, both the Applicant and the Opponent can present their arguments to the Registrar in written format. The Registrar will provide written notice, allowing the parties to file written arguments within one month. No arguments can be filed after this period without the Registrar's permission. An extension of time to submit arguments can be granted by the Registrar, with sufficient reasons and the consent of the other party. Generally, no further extensions will be granted after one extension has been given. Once the parties have filed their written arguments or the filing period has expired, the Registrar notifies each party that an oral hearing may be requested. Within one month of receiving this notice, a party can request the oral hearing, and the Registrar will provide a notice with the hearing's details. Hearings can also be conducted via teleconference if requested. If no oral hearing is requested, the Registrar will make a decision based on the written arguments. The Opponent has the initial burden of providing evidence for each ground of opposition, while the applicant has the burden to establish the registrability of their mark. Following the hearing, the Registrar will issue a decision either refusing the application, rejecting the opposition, or allowing the application in part. Conclusion The process of a trademark opposition can be a complex and intricate journey that requires careful attention to detail and goes from filing an Opposition, to filing a counterstatement, to presenting evidence, to engaging in cross-examinations, to submitting written arguments, and to appearing in an oral hearing. It is crucial to seek professional guidance from a trademark agent or lawyer to effectively navigate through the nuances of this process. Andrew Roy Legal offers both trademark agent and trademark lawyers’ services. Let us help guide you through the trademark opposition process, whether your application for a trademark is being opposed, or you need to oppose one that is infringing on your IP rights. Contact us today to learn more about how we can assist you. The information in this article is not legal advice and does not establish an attorney-client relationship. © 2023 Andrew Roy

If you enjoy this content, please consider signing up . Creating a member account is free, and you will · receive new content delivered directly to your inbox; · have exclusive access to members only content; · gain access to our online booking tool; · collect 500 bonus points in our points program. Introduction The recent announcement by Binance , a prominent cryptocurrency exchange, about its decision to exit the Canadian market has raised significant interest in the relationship between cryptocurrency and Canadian securities. The driving factor behind this move is the introduction of new regulations by the Canadian Securities Administrators (CSA) that affect the trading of stablecoins. The CSA recently introduced new Pre-Registration Undertakings (PRUs) that will apply to crypto asset trading platforms (CTPs) awaiting registration under securities legislation. The CSA Staff Notice 21-332 Crypto Asset Trading Platforms: Pre-Registration Undertakings Changes to Enhance Canadian Investor Protection (the Notice) was published on February 22, 2023 and put out additional requirements for enhance PRUS. These enhancements included, among others, restrictions on trading stablecoins. While there are other changes and requirements under the Notice, this article focuses on the understanding and the impact on stablecoins. Understanding Stablecoins in Cryptocurrency Stablecoins are a type of cryptocurrency designed to maintain price stability and reduce volatility. They have gained popularity as an alternative to highly volatile cryptocurrencies like Bitcoin, offering users a more stable store of value. Stablecoins can be pegged to different assets, such as fiat currencies or commodities, and are backed by reserves held by custodians, which is done through a smart contract system, or through real-world assets, such as gold, silver, or oil. Stablecoins offer a number of benefits to users in the cryptocurrency space. They have low volatility and can provide a more stable store of value compared to traditional cryptocurrencies. This makes them attractive for trading and hedging purposes, as well as allowing users to easily transfer between different types of assets without having to worry about exchange rate fluctuations. Stablecoins also make it easier for users to move into and out of cryptocurrency markets quickly, providing liquidity when needed. This is especially important in times of market volatility, enabling users to access their funds instantly if they need them. For businesses looking to accept payments in cryptocurrencies, stablecoins can be a great solution as they are less volatile than other types of digital currencies. In addition, stablecoins provide a number of advantages for developers looking to build decentralized applications (dApps) on the blockchain. They can be used as a store of value within the dApp and are attractive to users who may not want their funds subject to wild price swings. Furthermore, they can act as an intermediary currency between different blockchains, allowing developers to transfer tokens between them without having to worry about exchange rate fluctuations or fees associated with traditional exchanges. Stablecoins offer numerous benefits and make it easier for users and businesses alike to move in and out of cryptocurrency markets when needed. With their low volatility and ability to provide instant liquidity, these digital assets can help create greater stability in the market while making it easier for users to access and use digital tokens. Stablecoins provide a layer of stability that is needed to ensure the long-term success of cryptocurrency adoption, providing users with more confidence when investing in the digital asset market. The Notice and Regulation of Stablecoins The CSA's Notice introducered regulatory requirements for stablecoins, referred to as "Value-Referenced Crypto Assets" (VRCAs). The Notice emphasizes that the term "stablecoin" can be misleading due to instances where assets claiming to be stablecoins did not maintain their peg on trading platforms. The CSA's concerns revolve around investor protection, transparency of reserves, stabilization mechanisms, and governance of VRCAs. The notice indicates that stablecoins may be classified as securities and/or derivatives. Trading platforms must obtain prior written consent from the CSA to trade stablecoins and comply with various requirements to mitigate risks including the largest risk: the redemption or “run” risk. A "run" risk occurs when a large number of holders simultaneously request the redemption of their stablecoin tokens for the underlying assets (such as fiat currency) or demand access to the reserves that back the stablecoin. If the issuer is unable to meet these redemption requests promptly and in full, it can create a crisis of confidence and lead to a loss of value or even the collapse of the stablecoin. Per the Notice, the CTP must ascertain written consent from the CSA to enter into crypto contracts to buy or deposit a particular VRCA by ensuring that the VRCAs: Are a Fiat-Backed Crypto Asset; Where distributions of the Fiat-Backed Crypto Asset are made in Canada, these distributions must comply with Canadian securities legislation; The issuer of the Fiat-Backed Crypto Asset must maintain a reserve of assets with a market value at least equal to the value of outstanding units of the Fiat-Backed Crypto Asset at the end of each day; The reserve of assets is comprised of highly liquid assets, such as cash or cash equivalents; The reserve of assets is held by a qualified custodian in favour of the Fiat-Backed Crypto Asset holders; The reserve of assets is segregated from assets of the issuer and the assets of each class of other crypto asset issued by the issuer; The reserve of assets is subject to a monthly attestation and an annual audit from an independent auditor, copies of which are made publicly accessible in a timely manner; The redemption rights of the VRCA holder, directly or indirectly, against the issuer of the Fiat-Backed Crypto Asset, or the reserve of assets, are clearly articulated in policies and procedures and publicly disclosed; The issuer of the Fiat-Backed Crypto Asset maintains a plan for recovery and to support an orderly wind-down in case of a crisis or failure by the issuer or an affiliate of the issuer; The issuer of the Fiat-Backed Crypto Asset maintains effective governance practices; Key accurate information about the Fiat-Backed Crypto Asset is made publicly accessible in plain and non-technical language; and The CTP is not otherwise prohibited from allowing clients to enter into crypto contracts in respect of the Fiat-Backed Crypto Asset. Binance’s Exit from Canada Many cryptocurrency advocates around the world have often resisted these types of regulations because they increase the costs and burdens on the business, including reporting requirements, while stifling innovation. These regulations severally limit and reduce the way that the CPAs can operate and use stablecoins for their business. Additionally, these regulations undercut the ethos of cryptocurrencies, which is to create a financial system that is separate from the banker and the regulator. Stablecoins and other cryptocurrencies like Bitcoin are meant to be a self-governing and self-policing technology, where every transaction is visible on the blockchain. Due to the aforementioned regulations and the concerns expressed above, Binance made the decision to withdraw from the Canadian cryptocurrency market on May 12, 2023 . This exit of Binance poses a significant risk to the growth and development of the cryptocurrency industry within Canada. Relationship with the Bank Of Canada’s Digital Currency The interaction between stablecoins and the Bank of Canada's Digital Currency is a significant concern highlighted by the Notice and Binance's decision to withdraw from the Canadian market. In 2021, the Bank of Canada published a report titled " Central Bank Digital Currency and Stablecoins ," exploring the dynamics between stablecoins and the country's digital currency. Interestingly, the report points out that stablecoins have the potential to undermine the effectiveness of monetary policy while emphasizing the importance of the central bank's digital currency in maintaining competitiveness with private alternatives. A recent op-ed on CNBC titled " Stablecoin is the future of virtual payments. How wise regulation can foster its growth " delved into the potential relationship between central bank digital currencies and stablecoins. The article discusses how jurisdictions like China and possibly the EU may aim to maintain quasi-monopoly control over digital currencies through their central banks by restricting the presence of stablecoins in the economy. The recent Canadian regulations, including the Notice, appear to align with this trend. The concern of excessive regulation on stablecoins to the point of hindering their operations and creating a quasi-monopoly for the Canadian central bank digital currency seems to be an underlying strategy reflected in the Notice and the resulting exit of Binance from Canada. Conclusion The exit of Binance from the Canadian market highlights the impact of regulations on stablecoins and their potential consequences for the cryptocurrency industry. While regulations aim to enhance investor protection, the concerns surrounding the stifling of innovation and the impact on decentralized financial systems should also be considered. The relationship between stablecoins and central bank digital currencies further shapes this landscape, raising important questions about the future of cryptocurrency regulations in Canada. Andrew Roy Legal will continue to monitor and report on developments in this area. Do you have questions about cryptocurrency, digital currencies, or blockchain technology? Contact us today to learn more about how we can assist you. The information in this article is not legal advice and does not establish an attorney-client relationship. © 2023 Andrew Roy

If you enjoy this content, please consider signing up . Creating a member account is free, and you will · receive new content delivered directly to your inbox; · have exclusive access to members only content; · gain access to our online booking tool; · collect 500 bonus points in our points program. This blog post is a reproduction of an academic paper I wrote in 2015 for a law school course on law and technology. The paper has been modified to fit within the format of a blog post. It is reproduced here for ease of access and because AI in the legal profession is once again becoming a hot topic. If you are interesting in this topic, please also check out: Will AI Replace Lawyers? and Canada's Flawed Regulation of Artificial Intelligence Introduction Three different papers about information retrieval problems in the legal profession came out of the first A.I. and Law conference in 1987. Carole D. Hafner, Richard K. Belew, and Jon Bing were the authors of these papers. Schweighofer’s commentary on Bing’s paper argued that even though Bing dealt with similar problems as Hafner and Belew, his approach differed significantly from theirs. Instead of writing in “hard-core” A.I. language like Hafner and Belew, Bing, a lawyer, discussed the problem from a practical standpoint. This different approach complimented Hafner’s and Belew’s computer science background and better informed the legal A.I. that they were trying to develop. The integration between theory and practice illustrated that when the legal community actively engages with the A.I. and law community the development of legal A.I. meets the needs of both parties. On the other hand, if the two communities become alienated from each other, then legal A.I. develops without the input of the legal practitioner. This alienation creates a gap where the legal profession does not have the necessary understanding or knowledge of legal A.I. This gap creates problems for the legal profession as the legal A.I. intrudes into the courts, and judges must decide on how to address it. In the U.K. Court of Appeal, a justice rejected the use of Bayesian system, leading to a loss of its benefits and negatively impacting the administration of justice. In the U.S, the acceptance of predictive coding without the understanding of the lawyers forced litigators to use a technology that they did not understand, leading them to being unable to properly meet the interests of their clients. Overview The first part of this paper provides an overview of three approaches to developing legal A.I (logic trees, machine learning algorithms, and Bayesian systems). The second part of the paper draws on these approaches and investigates two cases studies of courts reacting to legal A.I: the rejection of Bayesian systems in a U.K. Court of Appeal and the wholesale acceptance of predictive coding in the U.S. With these case studies in mind, the third and final part argues for the need to train intermediaries to translate and communicate the assumptions, limitations, and benefits of legal A.I to the legal profession. It also recommends that the legal community push legal A.I. vendors toward open source principals to encourage proper investigation and development of their products. Part 1: The Nature of Legal A.I Background Legal A.I. does not try to replicate the internal reasoning process of a lawyer, nor does it try to mimic the lawyer’s behaviour. Developers of legal A.I. have designed them to act as agents that think or act rationally to produce the best possible outcome for a particular situation. A legal A.I. agent is composed of a program and architecture (this paper will focus exclusively on the program aspect, but it recognizes that the architecture is an important attribute in designing and differentiating legal A.I). The program is the formalization and the function that runs that formalization, and the architecture is the actual computing device that runs the program. Developers of legal A.I programs can adopt a variety of models, including but not limited to logic trees, machine learning algorithms, and Bayesian systems. These approaches are not legal A.I. in and of themselves, but only become legal A.I. when the developer uses them to automate a certain type of legal reasoning or process in order to solve a certain problem (this paper focuses on legal reasoning. The automation of legal processes would include things like legal transactions and forms [. . .] in all of the discussed methods, a legal expert helps create or guide the A.I to help ensure that it achieve its goal. Therefore, legal A.I. is often referred to as an expert system.). Logic Trees and Patent Litigation Patent litigation has proven to be a fertile area for legal A.I. development. Its systematic and technical nature makes it amendable to automation. Various companies have developed sophisticated legal A.I. that predicts patent outcomes (LexMachina is one of the more mature companies that employs legal A.I. for patent purposes). While many of these companies use machine learning algorithms to formalize patent claims, developers have also applied strict inference models. Kacsuk’s paper helps illustrate this approach. Strict inference reasoning tends to dominate critical legal thought, and Kacsuk shows how a developer can adopt this type of reasoning to design A.I. This approach is sometimes called “thinking rationally” or working through a “forward problem,” and it requires an expert to theorize and create a model based on precedent and then apply new cases to the model to produce an outcome. As a patents’ claim lawyer who has a background in logic and math, Kacsuk draws on precedent to create a mathematical model that predicts future E.U. patent claims. An individual can only patent a new invention if it is novel and involves an inventive step. The applicant communicates these attributes to the court in a complex patent claim (the example she provides is “navigation tool comprising a needle made of a ferromagnet"). Kacsuk break downs a sample patent claim into its symbolic parts and then adds judicial considerations like amendments and priority to round it out (for the patent claim, she breaks it down into “A = navigation tool B = needle C = ferromagnet). From these elements she creates a symbolic logic tree by applying calculus and symbolic logic principals. The execution of the logic tree that includes amendments, priority, and the atomic structure of a claim is found in Figure 1.19 The A.I. evaluates any new claims based on the dimensions that Kacsuk has encoded (the user will actually interact with the software that a computer scientist will program using Kacsuk’s model. The A.I. program, therefore, includes both the model and the execution of this formulization through the programming function). It thinks rationally about the claim by applying a model to a particular fact situation and then producing a rational conclusion using strict inference. This approach would likely encounter difficulties in areas where the legal reasoning has a less rigid framework. Even with patents, if the court decides to deviate from this structure, the A.I. cannot accommodate that deviation. Legal A.I. models like these generally do not adapt well to changing situations. Machine Learning and Legislative Interpretation Legal A.I. developers have used other methods of formalization like iterative machine learning to avoid this rigidity. This approach operates in reverse (or, inverse) as it takes observational data to produce a model that fits that data and then predicts the outcome based on that model. It has the ability to change as a legal expert codes its responses. Možina et al’s paper develops an argument-based machine learning system that aids in legislative interpretation. The developers want their A.I. to produce a set of rules that would match an ideal sets of rules, both of which determine whether an individual qualifies for welfare benefits in the U.K. (this ideal set of rules is only created to test the effectiveness of the A.I. It is not necessary for the A.I. to function). The machine learning A.I. uses both an ABCN2 algorithm and an iterative process. The A.I. first runs an algorithm CN2 to analyze the sample database to find a rule, remove the data that the rule covers, and then add that rule to a set of new rules. After the first run, the A.I. produces an imperfect set of rules. The developers, then, have an expert augment the algorithm with arguments to improve this first set (the experts provide arguments only for a piece of data that the A.I. continually misinterprets). The A.I. then runs the modified algorithm (ABCN2) to induce a new set of rules. After running through this iteration several times, the A.I. produces a set of rules very similar to the ideal list. The developers explain that “rules 1-5 are good rules, 6 and 7 have the right format, but the threshold is slightly inaccurate. The remaining four rules approximate the 10 ideal contribution rules.” The full comparison between the rules are seen in Table 1 below. Table 1 Ideal List 1. IF age <60 THEN qualified = no; 2. IF age <65 and sex = m THEN qualified = no; 3. IF any two of cont5, cont4, cont3, cont2 and cont1 = n THEN qualified = no; 4. IF spouse = no THEN qualified = no; 5. IF absent = yes THEN qualified = no; 6. IF capital >3000 THEN qualified = no; 7. IF inpatient = yes AND distance >750 THEN qualified = no; 8. IF inpatient = no AND distance £750 THEN qualified = no. Machine Generated List 1. IF capital >2900 THEN qualified = no; 2. IF age £59 THEN qualified = no; 3. IF absent = yes THEN qualified=no; 4. IF spouse = no THEN qualified = no; 5. IF cont4 = no AND cont2 = no THEN qualified = no; 6. IF inpatient = yes AND distance >735.0 THEN qualified = no; 7. IF inpatient = no AND distance £735 THEN qualified = no; 8. IF cont3 = no AND cont2 = no THEN qualified = no; 9. IF cont5 = no AND cont3 = no AND cont1 = no THEN qualified = no; 10. IF cont4 = no AND cont3 = no AND cont1 = no THEN qualified = no; 11. IF cont5 = no AND cont4 = no AND cont1 = no THEN qualified = no; The developers do not create an expert symbolic model, but classify and clarify A.I. with arguments from a legal expert. Rather than applying an expert model to the observables, the A.I. moves from the data to create a model. This A.I. can modify the model based on new data and learn from its previous mistakes. This approach provides it with an adaptability to new situations that the previous model lacked. Bayesian Systems and Criminal Trials Bayesian system determine probabilities of competing outcomes and are one of the most widely used approaches in legal A.I. Bayesian systems are “standard, successful in many real cases, and logically well justified.” Computer scientists, engineers, and mathematicians also use this system. Bayesian systems are an amalgamation of incident diagrams (also called directed acyclic graphs) and mathematical theory (i.e. Bayes’ theorem) (Kaptein, Prakken, & Verheij describes Bayes’ theorem as the “probability ratio of two competing hypothesis is the product of the ratio of the two competing hypothesis before the evidence came in, multiplied by the likelihood ratio of the evidence under the two competing hypothesis”). Incident diagrams allow a developer to systematically model relationships, and the mathematical theory calculates the different probabilities for the outcomes that arise out of the relationship. Bayesian networks systematically model complex relationships and, based on those complex relationships, calculate probabilities for certain outcomes (Franklin remarks that the main message of a Bayesian system is: “the verification of a (non-trivial) consequence renders a theory more probable” ). Developers of legal A.I. have adopted this approach to model complex legal relationships and estimate probabilities for certain legal outcomes. Vlek et al.’s paper uses a Bayesian system to model the legal reasoning from a Dutch criminal trial in order to compare the probabilities of two competing scenarios. They illustrate how Bayesian systems can solve practical legal issues with probabilities by constructing an incident diagram based on the narrative of the case and breaking the narrative up into two scenario nodes: the scenario where Beekman (“B” accused #1) kills Leo (“L” the victim) and the scenario where Marjan (“M” accused #2) kills L. Figure 2 models the reasoning behind “M killing L because of a cannabis operation”: Figure 2 The developers place the reasons that support each argument in a schematic scheme that models their relationships. A sub-scenario node supports the scenario node and a sub-sub-scenario node supports the sub-scenario node. “M actually had a cannabis operation” or that “M called another suspect to help her with the body” are reasons that support the main scenario node. “M had a false contract” is a reason that supports the sub-scenario idiom “L was to be the front of the Cannabis operation.” Vlek et al. must also assign probabilities for each and every scenario node (they actually assign likelihood ratios and prior probabilities, but to clarify the idea of probabilities for their scenarios, they reduce the example to just probabilities. A discussion of the difference between likelihood ratios and prior probabilities will occur in more depth in the following section). For example, “L was in state of impotence” supports the sub-scenario node “M drugged L.” The toxicology report stated that because L had alcohol and high amounts of Temazepam in his blood, it is likely he was impotent. Therefore, the probability that “if M drugged L, L was in state of impotence” is high (for example, 0.99). They run the entire network in a software that executes the model and probabilities, producing the probabilities of 0.25 that M was the killer and 0.7 that B was the killer. The A.I’s functioning depends on the developer’s understanding of legal reasoning because the A.I. identifies non-trivial outcomes based on the relationship between the evidence and the legal narrative that the expert creates. Part 2: Responses from the Court Legal A.I.’s drive to achieve the best outcomes for a particular situation means that legal A.I. has the potential to aid legal professionals in their profession. Kacsuk’s A.I. helps lawyers predict the success of patent claims. Možina et al.’s A.I. assists legal experts in quickly determining whether or not a person meets the legislative requirements for welfare benefits. Vlek et al. helps a litigator determine the probability that one situation that informs the result of a criminal trail happened over another. Based on these models, legal A.I. has the potential to improve the administration of justice and help a lawyer represent her client’s interest. In an ideal world, lawyers and judges would understand both the strengths and limitations of legal A.I. and use them in the right situations to produce the best possible outcome. In the real world, lawyers and judges do not have this understanding, and as a result, do not use legal A.I. in the right situations to produce the best possible outcomes. Two case studies illustrate this fact. In the U.K., a Court of Appeal rejected the use Bayesian systems and the benefits it offers. A string of U.S. decisions embraced predictive coding, but did so without first ensuring that lawyers had the correct information and research about the A.I. to properly understand its implications. Without this understand, the lawyers could not properly meet the needs of their clients. Rejection R v. T and Bayesian Systems A U.K. Court of Appeal rejected a forensic evidence expert’s use of Bayesian systems in weighing the certainty of scientific evidence in R v T. In the case, the court required a forensic expert to determine the probability that a type of footwear made a specific mark. To meet this requirement, the expert provided testimony on the probability of this event occurring by using a Bayesian system (this type of legal A.I. operates at the fringes of the nature of legal A.I. because weighing the validity of scientific information is not something the average legal profession does. However, this case study is still instructive because of the implications this decisions has on the development of “purer” forms of legal A.I. that use Bayesian systems to automate legal reasoning (like the Vlek et al example which tried to automate a type of legal reasoning used at a criminal trial)). In response to the expert’s testimony, the justice ruled that “no likelihood ratios or other mathematical formula should be used in reaching whether the footwear made a mark beyond the examiners’ experience.” Likelihood ratios “should not be used in evaluating forensic evidence, except for DNA” and “possibly other areas where there is a firm statistical base.” By dismissing likelihood ratios, the justice undermined the use of Bayesian system in a court. This decision did not just prevent forensic evidence experts from using this system, but it prevented developers of legal A.I. from using it for more experimental purposes that certainly lack firm statistical foundations. The judge combined the idea of assessing the probabilities of the proposition and the strength of evidence for the proposition, and as a result, made the logical error that if probabilities of a formula cannot be properly expressed, then they negate the validity of the relationships described by the formula. The calculation of a Bayesian posterior probability depends on both a likelihood ratio and prior probabilities. The likelihood ratio expresses the relationship between two competing hypotheses based on the evidence (i.e. the probability of the evidence occurring for hypothesis #1 and for hypothesis #2). On the other hand, the prior probability expresses the chance of each hypothesis actually occurring (i.e. what is the likelihood that the hypothesis is true? [. . . ] Berger provides an illustrative and accessible example of the relationship between the likelihood ratio and prior probability). Therefore, the likelihood ratios is the strength of the evidence for that proposition and the prior probability is the determination of the likelihood of that proposition. Based on this fundamental misunderstanding of statistical principals, the justice incorrectly assumed that these two concepts had the same role in a Bayesian system.
Experts set likelihood ratios based on the strength of the evidence, which is often based on objective standards. However, they set prior probabilities based on the likelihood of the hypothesis being true. In other words, experts create prior probabilities based on their own judgement of the situation, and therefore, even forensic evidence experts sometimes lack a hard, empirical base to ground their prior probabilities. Experts account for this less-than-objective measurement by setting their prior probabilities conservatively. The developers in the Vlek et al. case were trying to predict probabilities of entire narrative scenarios, so they had to set the prior probabilities for each scenario idiom at 0.01. They recognized that the criminal law system requires that all defendants be presumed innocent until proven guilty, and therefore, they needed to set the prior probabilities of each scenario idiom at the same conservative value. The prior probabilities lacked a hard empirical base for these developers because the criminal law system restricted the researchers from setting them at any other figure. Despite this limitation, the likelihood ratios still incorporated the scientific evidence from the trial into the calculation, and the theorem still properly calculated the relationship between the likelihood ratios and prior probabilities. Even with a more experimental Bayesian system that cannot properly express the prior probabilities with a firm statistical basis, the posterior probabilities still correctly illustrate the validity of the relationship between the evidence and the narrative. Even in forensic evidence cases, Bayesian probabilities do not provide objective measurements of the situation occurring (to ask for such a result would be akin to asking a lawyer to tell the court with absolute certainty who actually committed the crime). Bayesian systems calculate probabilities based on a formalized method that roots out errors in human reasoning. Forensic evidence experts use Bayesian system because they cannot calculate the likelihood that a certain piece of evidence resulted in a certain situation on their own (imagine trying to calculate the probability that one shoe made one particular mark when you have a thousand shoes that can produce a thousand different marks). The system clarifies the value of scientific evidence to better inform the decision of the trier of fact (as Kaptein, Prakken, & Verheij argue, “the model serves as powerful conceptual framework, which makes explicit what data are required to arrive at a source attribution statement”). The developers in Vlek et al. used the Bayesian system to remove human reasoning biases. The narrative of the Dutch case had encouraged tunnel vision and a “good story” emotionally manipulating the trier of fact (which, perhaps, lead Marjan to be unfairly convicted at trial). The developers had used the Bayesian system to try and adjust for these biases and to show that when they removed these biases, Beekman was more likely to be the killer (this does not mean Bayesian systems have no problems of their own, including problems of circularity, issues with weighing evidence, and being susceptible to the reference class problem. There concerns do not invalidate their use, but are presumptions that the court should be aware of in weighing the probabilities that these systems produce. This conclusion also does not mean that Beekman was absolutely the killer, but it provides the trier of fact with one more tool in determining a verdict).
R v T prevents forensic evidence experts from properly weighing scientific evidence and shut off more experimental legal A.I. that cannot meet the rigorous requirements of statistical certainty from developing. This decision prevents legal A.I. developers from using Bayesian systems to automate legal reasoning and challenge conceptual biases. It negatively impacts the administration of justice and prevents legal A.I. from potentially improving certain types of legal reasoning that have a direct impact on determining the verdict of a trial. Acceptance: Da Silva Moore and Predictive Coding Courts can also accept legal A.I. without fully understanding its presumptions, limitations, and benefits. The United States mandated the use of predictive coding without first requiring that lawyers full comprehend this type of A.I. Da Silva Moore, a federal decision, established that a party could use predictive coding for discovery. Global Aerospace followed that judgement, and the court in Kleen Products, LLC v. Packaging Corporation of America pushed the principals forward by granting an order for the defendants to redo their e-discovery with predictive coding. The judge in EORHB, Inc. ordered the parties to use predictive coding or show why it ought not to be used. This wholesale acceptance of predictive coding for e-discovery within the United States has made predictive coding a central concern for litigators (the state of predictive coding is considerably less certain in Canada).
Lawyers have not completely embraced this legal A.I. despite its widespread acceptance from the courts. The Sedona Conference lists three general reasons for why litigators have refused to use predictive coding. First, litigators are concerned that “computers cannot be programmed to replace the human intelligence required to make complex determinations on relevance and privilege. Second, lawyers have a perception “that there is a lack of scientific validity of search technologies necessary to defend against a court challenge.” Third, litigators have a “widespread lack of knowledge (and confusion) about the capabilities of automated search tools” (the concerns that the Sedona Conference raise are that predictive coding would miss some documents found in a straight keyword search and even with the extra effort with predictive coding, it will still miss problem documents). These three concerns ought to be understood within the context of each other as the lack of empirical research and information about the structure of predictive coding A.I. contributes to the lack of knowledge that litigators have about this area.
Very few scholars have actually addressed these three concerns because they have chosen to focus almost exclusive on the iterative process rather than studying the iterative process in tandem with the predictive coding algorithms (an expert litigator will take the documents that the A.I. has coded as either relevant or irrelevant and relay that information back to the A.I., so during its next attempt, it can more accurately and precisely code the documents). Nicholas Barry and Baron & Burke both provide an excellent overview of the importance of studying the iterative process that informs predicting coding A.I. However, neither address the impact that predictive coding algorithms have on the iterative process and on the predictive coding A.I. itself. Baron & Burke argue that one must “recognize, first and foremost, the importance of the process that manages the task,” and later conclude that “a failure to employ a quality e-discovery process can result in failure to uncover or disclose key evidence." By focusing solely on the iterative process in judging quality, Baron & Burke fail to recognize that the predictive coding algorithms also significantly contribute to quality of the A.I.. Later in the paper, they present a list of ways to ensure quality in predictive coding but again focus solely on the iterative process itself with no mention of the algorithms (this list includes judgmental sampling, independent testing, reconciliation techniques, inspection to verify and report discrepancies, and statistical sampling). In his paper, Barry analyzes Baron & Burke’s list and argue that statistical sampling is the preferable approach to ensuring the quality of the predictive coding process because it evaluates the quality of the actual results. This argument still misunderstands that judging the results on their own does not comment on effectiveness of the algorithm, the iterative process, and the predictive coding A.I. Empirical research needs to account for both the iterative process and the algorithm to meet the concern that “that there is a lack of scientific validity of search technologies necessary to defend again a court challenged.”81 The E-Discovery Institute conducted a wide-ranging survey on predictive coding A.I. to increase vendor transparency. The survey polled a list of predictive coding vendors, and Gallivan Gallivan & O’Melia (“GGO”) responded to a question that asked them to describe their process: “Collect and process records; extract content and placed in a repository, store references in a database. Consolidate duplicates. Extract text or OCR, compare text content to create a similarity vector, store results […] The reviewers identify groups known to be responsive and then we associate other records that are “most like” those records based on the similarity vector. Reviewer decisions define the actual mark of the documents vs. the mark suggested by our system. As new waves of data arrived, they are placed in groups based on similarity vectors generated for that data.” This quote illustrates that predictive coding is an integration of both the iterative process and the algorithm (i.e. the “similarity vector”). The statistical sampling approach that Baron & Burke’s and Barry advocated would not actually measure the impact of the similarity vector on the quality of the A.I. Imagine that “y” is a competing predictive coding vendor, and a researcher decides to evaluate the effectiveness of both “y” and GGO. The researcher finds that GGO has a success rate of 65% and ‘y’ has a success rate of 60%. If a litigator saw these results, she would likely choose GGO. However, imagine now that the researcher has access to both vendors’ A.I. and has somehow removed the algorithms from each A.I. and placed vendor ‘y’’s algorithm into GGO’s A.I and vice versa. After re-running the test, ‘y’’s A.I. now has a success rate of 20% and CGO’s has a success rate of 85%. These results show that ‘y’ actually had the better algorithm, but just a slightly less effective iterative process. GGO had a terrible algorithm (or “similarity vector”) but had a marginally better iterative process. The litigator might now refuse to use GGO and choose vendor ‘y’ (and ask ‘y’ to slightly modify its iterative process). The original statistical sampling would have lead the litigator to choose the wrong A.I. for her client (and not meet the client’s best interests) because it did not adequately measure the quality of the algorithm, the iterative process, and the predictive coding A.I.
Inspecting the predictive coding algorithm will also help address the concern that this type of A.I. “cannot be programmed to replace the human intelligence required to make complex determinations on relevance and privilege. Predictive coding A.I. has actually had difficulties with replacing a lawyer’s reasoning process for determining the relevance and privilege of a document. The legal A.I. community has used the case of Popov and Hayashi to address this problem. The court in Popov v Hayashi needed to rule on who possessed a baseball. The court considered cases that dealt with hunting foxes, whales, and ducks to arrive at their decision. The reasoning process that requires a judge to answer this question requires that she understand that a case about a fox, a whale, and a baseball have a high degree of similarity or relevance. Legal A.I. developers have struggled with formalizing this “common-sense reasoning.” Franklin devises an argument that visualizes the reasoning that this problem requires: Premise 1: a has features f1, f2, . . . , fn. Premise 2: b has features f1, f2, . . . , fn. Premise 3: a is X in virtue of f1, f2, . . . , fn. Premise 4: a and b should be treated or classified in the same way with respect to f1, f2, . . . , fn. Conclusion: b is X. A lawyer normally provides the information in manual review to support Premises 3 and 4. Yet, the validity and accuracy of predictive coding A.I. depends on how the algorithm deals with this very problem. 87 Looking only at the iterative process will not provide the litigator with a satisfactory answer to this conceptual problem. Discovering how one vendor deals with this problem versus another requires an investigation of the algorithm. Jason R. Baron and Paul Thompson commented that “one can search in vain through a vast amount of proprietary literature [dealing with predictive coding software] without citation or grounding to AI or IR research.” Six years later, the legal community still has not yet solved this problem. The court mandating the use of predictive coding has forced lawyers to adopt A.I. that they do not understand. Research in this area needs to focus on the iterative process and the algorithm to provide litigators with this understanding. If a lawyer does not have this research, then she will likely choose a predictive coding vendor that does not best serve her client’s needs. Part 3: Bridging the Gap between Theory and Practice The case studies from the U.K. and American courts illustrate that legal professionals do not have the necessary understanding of legal A.I. to take full advantage of its benefits. This problem has negatively impacted both the administration of justice and a lawyer’s duty to protect her client’s interest. To deal with the negative effects of the creep of legal A.I. into the courtroom, the legal profession needs to offer realistic and workable solutions to ensure that legal practitioners receive the information they need about legal A.I. to make informed decisions. Training and developing intermediary experts to properly inspect and investigate legal A.I. with grounding in legal A.I. research would help bridge the information and knowledge gap between theory and practice. Encouraging legal A.I. developers to adopt open source principals would further ensure that intermediaries could properly conduct research into a vendor’s legal A.I. and help ensure that judges and lawyers receive the necessary background and information on this complex and foreign area. Training the Intermediary Expert Judges and lawyers likely do not have the background, training, or time to conduct the necessary research to have a nuanced understanding of legal A.I. Hard-core A.I and law scholars are also less concerned with the practical benefits of one vendor software over another, and more concerned with producing general theoretical principals and formalizations that drive legal A.I. Legal professionals should not need to learn a new language overnight, and A.I. and law scholars should not suddenly need to generalize and dilute their writing for the legal profession. Intermediary scholars versed in both legal A.I. and legal practice would offers a realistic solution and bridge this information and knowledge gap. Intermediaries of a certain kind already exist. McGinnis & Pearce offer practical insight into areas where legal A.I will have a significant impact on the legal profession (They identify how general technologies and developments like machine intelligence, IBM’s Watson, and Moore’s Law (i.e. processing speeds increasing and storage costs decreasing) are transforming discovery and legal analytics). Cooper discusses how courts could use Watson to correctly identify the context and meaning of a word in a piece of legislation (In talking about Watson, she comments that “Watson runs on a cluster of Power 750™ computers—ten racks holding 90 servers, for a total of 2880 processor cores running DeepQA software and storage”). Katz explains how legal A.I. use structured and semi-structured data, natural language processing, information retrieval, knowledge representation, and machine learning (the remaining parts of his paper provide some concrete examples of areas where A.I. could affect the legal profession, including predicting judicial decisions, patent disputes, securities fraud class actions, and predictive coding). While these writers do not use mathematical and computer science jargon and make these concepts more accessible to legal practitioners, they also fail to properly engage with A.I and law scholarship. When discussing predictive coding or machine learning, these writers focus almost exclusively on describing general technologies (e.g. big data or decreased memory cost) and then speculate on their potential legal application.93 This approach ignores the vast amount of legal A.I research available and prevent these writers from critically analyzing specific legal A.I. Fenton et al. advocated for a radical rethink on how to communicate Bayesian systems to the legal profession. They argued that intermediary scholars or experts need to have legal practitioners accept that they only need to question the prior assumptions that go into the calculations and not the accuracy or validity of the calculations given those assumptions. Even though they were referring only to Bayesian systems, their suggestion provides a good starting place on how to properly communicate legal A.I. to legal professionals. If intermediaries could communicate the presumptions, limitation, and benefits of legal A.I. based on sound A.I. research, they could effectively bridge the information and knowledge gap between the two disciplines. If GGO had released the details of their algorithm, then an intermediary versed in legal A.I. could break it down and present its strengths, weaknesses, and presumptions to a legal practitioner. An intermediary could also synthesize the A.I. research on Bayesian systems and present it to judges in a way that focuses on the presumptions, limitations, and benefits of this approach. When more experimental legal A.I. enters the courts, justices could use this information to better anticipate their usefulness. The legal profession needs to support the development of intermediaries. Law schools can foster a law student’s interest in A.I. by offering courses that provide a mathematical, statistical, and computer science foundation to supplement the development of a law student’s legal reasoning and knowledge. Law faculty specialized in these areas could teach these courses, or professors from mathematical, statistical, or computer science departments could offer courses through the law school. Law societies could offer educational opportunities to lawyers interested in these areas, and law firms and governments could provide professional opportunities for intermediaries, instead of potentially offering these positions to experts in other fields. Open Source Legal A.I A closed approach to legal A.I. would make rigorous analysis and inspection of A.I. very difficult for intermediaries. Opening legal A.I. would provide a solution to this problem. If the legal profession encouraged legal A.I. developers to use open-source technology, intermediaries could have a way to analyze and discuss legal A.I. products in the marketplace (because this paper focuses on A.I. programs, it emphasis open-source software. However, as legal A.I. architecture also defines the design and development of legal A.I., this paper also encourages open-source hardware, which has very similar principals as open source software). Open-source legal A.I. could have a broad definition that could cover a range of situations. The Open Source Initiative lists 10 elements that contribute to the degree that a software is open. These elements include: Free redistribution, open source code, allowing for derived works, protecting the integrity of the author's source code, no discrimination against persons or groups, no discrimination against fields of endeavor, license must not be specific to a product, license must not restrict other software, and license must be technology-neutral. The legal profession does not need to require that legal A.I. developers satisfy all 10 requirements to still achieve the objectives of allowing for the proper investigation and inquiry of legal A.I. Not restricting the availability of the source code, allowing for derived works, and allowing for distribution of licenses would be the most relevant to this mandate. Freeing the source code would allow intermediaries and A.I. and law scholars to see how legal A.I. functions. It would allow them to locate any presumptions that the technology relies on as well as identify any of its limitations and benefits. Allowing for derived works further supports independent peer review because experts often need to experiment and modify code to see how it effects the performance of the A.I. Requiring open distribution licenses prevents companies from closing up their A.I. through indirect means like requiring a non-disclosure agreement. If the vendor allows the scholar to inspect and test the software, but not publish the results, the purpose of opening up legal A.I. is defeated. While observing the remaining 7 principals are not necessary to achieve its objective, the legal profession should revisited these principals if any issues arise that would prevent intermediaries from inspecting or publishing on legal A.I. As vendors spend billions of dollars to develop legal A.I., any concentrated push from the legal profession towards open source will likely encounter resistance. The legal community should emphasize that open source technologies has the potential to commercially benefit those who develop it and provide any further incentives or information to encourage this development (IBM, Sun Microsystems, and Orcale are all prominent examples of companies that have invested in open source software and have had profitable returns). Numerous studies have also demonstrated which type of companies actually benefit from embracing open source software (benefiting from the switch depends on whether the company produces hardware or software, or whether most of their property is protected by patents or trademarks. The scale and the type of development process, and the size and approach of the company also effect the potential for profitability). They have also provided ways for companies to make the switch. The legal profession could use this information to educate companies on proper ways to develop and plan for adopting open source principles. While opening up legal A.I. depends on a variety of factors (these factors include organizational and business structures, the form of A.I. developed, and the type of intellectual property governing it), scholarship on open-source software clearly shows that is possible for companies to make this change and gain both significant social and economic value from the transformation. This knowledge should provide a sufficient motivation for the legal profession to investigate the feasibility of its implementation. Conclusion Legal A.I. does not try and think or act like a lawyer; instead; it thinks or acts rationally to achieve the best outcome according to the situation. The nature of legal A.I requires developers to adopt different approaches to formalization, including symbolic logic trees, machine leaning algorithms, and Bayesian systems. As legal A.I. becomes more sophisticated and enters the courts, judges must rule on its applicability in the legal system. Their lack of knowledge about legal A.I. has led to problematic decisions. A UK Court of Appeal justice had disallowed forensic evidence experts from using Bayesian systems, undermining the development of these networks for other legal A.I. purposes. A string of decisions in the United States accepted predictive coding but did not ensure that lawyers had the information available to properly use the A.I. to ensure they meet the best interests of their clients. To deal with this lack of understanding, the legal profession could develop intermediary scholars or experts to bridge the information and knowledge gap between the theory and practice of legal A.I. It could also push legal A.I. developers to open up their A.I. to encourage the proper investigation and study of their products. The past two decades have seen a niche discipline comprised of mathematicians, statisticians, philosophers (and the odd lawyer) creep into the courts and the average legal professional’s life. Judges must now rule on once obscure concepts like Bayesian systems, and litigators must understand machine learning algorithms to properly meet their clients’ needs. McGinnis & Pearce argue that this disconnect will cause a disruption in the legal profession akin to the one that journalism has undergone. The Canadian Bar Association proclaims that “with technology, clients will increasingly realize that lawyers are not essential for all questions touching the law — in some respects, they are fungible.” William Caraher and Cooper argue that IBM’s Watson will replace lawyers and judges. These events force the legal profession to decide how to deal with the rise of legal A.I. If the profession decides to resists the advances of legal A.I., it may encounter a grim dystopian. If it decides to embrace its ever expanding horizon, it may welcome a bold new world. Bibliography JURISPRUDENCE: UNITED STATES EORHB, Inc. v. HOA Holdings, C.A. No. 7409-VCL (Del. Ch. Oct. 15, 2012) Order Approving the Use of Predictive Coding for Discovery, Global Aerospace, Inc. v. Landow Aviation, L.P., Consolidated Case No. CL61040 (Loudoun Cnty., Va. Apr. 23, 2012) Kleen Products v. Packaging Corp. of America, 2012 U.S. Dist. LEXI 139632 (ND Ill. Sep. 28, 2012) Monique Da Silva Moore et al. v. Publicis Groupe & MSL Group, 868 FRD (2d) 137 (SD NY 2012) Popov v. Hayashi, 2002 WL 31833731 (Ca. Sup. Ct. 2002) JURISPRUDENCE: UNITED KINGDOM R v T 2007 EWCA Crim 2439, All ER (D) 240 SECONDARY MATERIALS: ARTICLES Alexy, Oliver, Henkel, Joachim & Wallin, Martin W. “From closed to open: Job role changes, individual predispositions, and the adoption of commercial open source software development” (2013) 42:8 Research Policy at 1325-1340. Baron, Joseph R &. Burke, Macyl A (eds), “The Sedona Conference commentary on achieving quality in the e-discovery process: A project of the Sedona Conference” (2009)10 Sedona Conference J at 299. Barry, Nicholas "Man Versus Machine Review: The Showdown between Hordes of Discovery Lawyers and a Computer- Utilizing Predictive- Coding Technology" (2013) 15:2 Virginia J of Ent & TL at 343. Bench-Capon, Trevor et al, “A History of AI and Law in 50 Papers: 25 Years of the International Conference on AI and Law” (2012) 20:3 AI & L at 1. Berger Charles E.H. et al “Evidence evaluation: A response to the court of appeal judgment in R v T.”(2011) 51:2 Science & Justice at 43. Caraher, William “Is this computer coming for your job? IBM's Watson supercomputer shows tremendous potential to revolutionize the legal profession” (2015) Nat’l LJ at 16. Cooper, Betsy, “Judges in Jeopardy!: Could IBM’s Watson Beat Courts at their Own Game?” (2011) 121 Yale LJ at 87. Fenton, Norman et al, "When ‘neutral’ evidence still has probative value (with implications from the Barry George Case)"(2014), 50:4 Science and Justice at 274. Fosfuri, Andrea, Giarratana, Marco S. & Luzzi, Alessandra “The Penguin Has Entered the Building: The Commercialization of Open Source Software Products” (2008) 19: 2 Organization Science at 292. Franklin, James, "Discussion Paper: How Much of Commonsense and Legal Reasoning is Formalizable? A Review of Conceptual Obstacles," (2012) 11:2-3 L, Prob & Risk at 225. Kacsuk, Zsófia, "The Mathematics of Patent Claim Analysis," (2011) 19:4 AI & L at 263. Katz, Daniel Martin. "Quantitative Legal Prediction - Or - how I Learned to Stop Worrying and Start Preparing for the Data- Driven Future of the Legal Services Industry. (Innovation for the Modern Era: Law, Policy, and Legal Practice in a Changing World)," (2013) 62:4 Emory LJ at 909. Kilamo, Terhi, et al. “From proprietary to open source—Growing an open source ecosystem” (2012) 85:7 The Journal of Systems & Software at 1467. Lee, Changyong; Bomi Song & Yongtae Park, "How to Assess Patent Infringement Risks: A Semantic Patent Claim Analysis using Dependency Relationships," (2013) Technology Analysis & Strategic Management 25:1 at 23. McGinnis, John O. & Russell G. Pearce, "The Great Disruption: How Machine Intelligence Will Transform the Role of Lawyers in the Delivery of Legal Services.(Colloquium: The Legal Profession's Monopoly on the Practice of Law)," (2014) 82:6 Fordham L Rev at 3041. Možina, Martin et al, "Argument Based Machine Learning Applied to Law," (2005) 13:1 AI & L at 53. Murphy, Tonia Hap, "Mandating use of Predictive Coding in Electronic Discovery: An Ill‐ Advised Judicial Intrusion," (2013) 50:3 Am Bus LJ at 609. Polanski, Arnold “Is the General Public Licence a Rational Choice?” (2007) 55:4 The Journal of Industrial Economics, at 691. Schweighofer, Erich “Designing text retrieval systems for conceptual searching”, Comment, (2012) 20:3 AI & L, 20(3) at 9. Trevor Bench-Capon et al, “A History of AI and Law in 50 Papers: 25 Years of the International Conference on AI and Law” (2012) 20:3 AI & L at 1. Vlek Charlotte S. et al “Building Bayesian networks for legal evidence with narratives: A case study evaluation” (2014) 22:4 AI & L at 375. SECONDARY MATERIALS: CONFERENCE PAPERS Baron, Jason R. & Thompson, Paul (2007) “The search problem posed by large heterogeneous data sets in litigation: possible future approaches to research” (Paper Delivered at the Eleventh International Conference on Artificial Intelligence and Law, 2007) .New York: ACM Press, 2007 at 42. Belew, Richard K. “A connectionist approach to conceptual information retrieval” (Paper delivered at the First International Conference on Artificial Intelligence and Law, 1987).New York: ACM Press, 1987, at 116. Bing, Jon “Designing text retrieval systems for conceptual searching” (Paper delivered at the First International Conference on Artificial Intelligence and Law, 1987) New York: ACM Press, 1987, at 51. Hafner, Carol D. “Conceptual organization of case law knowledge bases” (Paper delivered at the First International Conference on Artificial Intelligence and Law, 1987). New York: ACM Press at 35. SECONDARY MATERIALS: MONOGRAPHS Burke et al. E-discovery in Canada, 2nd ed (Markham, Ont.: LexisNexis,2008). Kaptein, Hendrick, Prakken, Henry & Verheij, Bart. Legal evidence and proof statistics, stories, logic (Applied legal philosophy). (Burlington, VT: Ashgate, 2009). Russell Stuart J. & Norvig Peter, Artificial Intelligence: A Modern Approach, 3rd ed. (Upper Saddle River, N.J.: Prentice Hall, 2010). SECONDARY MATERIALS: ONLINE SOURCES Canadian Bar Association, “Futures: Transforming the Delivery of Legal Services in Canada” (August 2014) online: <http://www.cbafutures.org/CBA/media/mediafiles/PDF/Reports/Futures- Final-eng.pdf?ext=.pdf> E-Discovery Inst., eDiscovery Institute Survey on Predictive Coding, eDiscovery Inst., 6-10 (Oct. 1, 2010), online: <http://www.ediscoveryinstitute.org/images/uploaded/272.pdf> Hebert, Sarah et al. “Bayesian Network Theory” (29 November 2007), The Michigan Chemical Process Dynamics and Controls Open Text Book, online: <https://controls.engin.umich.edu/wiki/index.php/Bayesian_network_theory> The Open Source Initiative, “The Open Source Definition” (22 March 2007), online: https://opensource.org/osd [Open Source Initiative].

If you enjoy this content, please consider signing up . Creating a member account is free, and you will · receive new content delivered directly to your inbox; · have exclusive access to members only content; · gain access to our online booking tool; · collect 500 bonus points in our points program. Current Legal AI The advent of artificial intelligence (AI) and technology has been a topic of debate in various industries, including the legal profession. The question of whether AI technology can replace lawyers has been discussed for years, and it remains a hot topic today. In 2015, when I was in young law student making my way through law school, I wrote a paper on this very topic entitled Embracing a Bold New World: The Rise of Legal A.I. in the Legal Profession . Since then, the landscape has changed significantly, and as a practicing lawyer, I have always kept an eye out for the development of legal technology in order to improve my practice. Fast forward to 2023, and we see new articles and commentary pieces that are once again asking the question: can AI replace lawyers? The NY Times recently wrote an article entitled A.I. Is Coming for Lawyers, Again . A group of researchers published a working paper entitled GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models . The research paper looks closely at how AI could disrupt the legal profession. It is essential to note that the legal profession involves handling a large volume of work. People can sometimes see lawyers as artists that work deep into the night creating a brilliant new argument through sheer inspiration, grit, and determination, and then surprise the courtroom with an elegant and impassioned speech. While such a depiction can make a good movie or TV show, the reality of legal work bears no resemblance to this Hollywood invention. Legal work is highly systemized because of the sheer volume and complexity of the work. With the development of legal AI, this systematization has become more sophisticated and streamlined. Lawyers and law firms that have embraced these new technologies and have found ways to incorporate them into their practice have become better positioned to manage the volume that goes along with a law practice. Legal AI has allowed lawyers to offload work that was generally completed by humans onto programs that can analyze information, identify patterns, and make predictions in a much more efficient and cost-effective manner. Such an approach has enabled lawyers to make more informed decisions and provide better advice for their clients while reducing overhead. This development increased efficiency for law firms and created a more competitive legal industry. One area where we have seen legal AI make significant impact on the legal practice is in eDiscovery, which involves the analysis of large volumes of data, such as emails and documents, to identify relevant and privileged information for litigation. AI-powered tools like these are used to analyze tens of thousands of documents to identify patterns and correlations that may not be apparent to a human reviewer. This process saves a considerable amount of time and resources, as well as reduces the risk of errors and inconsistencies. In the past, this work was often performed by a whole team of junior associates, but law firms have now delegated these tasks to specialized eDiscovery technologies and the staff that know how to operate these programs. We have also seen significant development in legal AI as it relates to contract management. With contract management, legal AI analyzes contracts and extracts key information, reducing the risk of errors and ensuring compliance with case law, legislation, and regulations. These technologies identify key terms and clauses in contracts and flag any potential issues or risks for lawyers to review. This AI can be particularly useful for large law firms that handle a high volume of contracts. A similar type of legal AI has developed for document management. Instead of having a team of legal assistants compile a variety of documents for a commercial transactions using word documents which are scatted across different folders, these legal technologies systemize the process by using document templates, which are then automatically compiled into a set of transactional documents for the lawyer to review. These technologies increase the efficiency and accuracy for a variety of different transactional work, allowing law firms to process commercial or real estate transactions much faster. Many more types of legal AI exist, which you can read more about in Embracing a Bold New World: The Rise of Legal A.I. in the Legal Profession . Legal AI and LLM With the invention and development of Large Language Models (“LLM”) and ChatGPT, we may have entered into a new chapter in legal AI. The question that is now being considered is: how much more of the complicated and complex legal work can AI replace? Back when I researched legal AI in law school, I noticed that many commentators and academics found a common issue for AI. The systemization of legal language and reasoning was beyond the capabilities of the current AI technology. Legal reasoning and language are very different from that of computer programming and technical writing. They are often heavily based on context that is ambiguous and not readily apparent, which is something that AI, at that time, really disliked. As a result, legal AI targeted processes that did not contain such contextual legal reasoning and, instead, focused on systemized processes like eDiscovery. However, with the development and deployment of LLMs and ChatGPT, this bridge may be crossed. Current LLMs are much better with contextual natural language processing, and an LLM that is trained on legal documents may better approximate legal reasoning. If that is the case, we could see a systemic shift in the legal profession where AI is completing more sophisticated tasks. Many of these legal AI technologies that leverage LLM are still in the developmental beta stage. We do not know how effective these new technologies will be and how much more legal work and reasoning they can replace. Two such promising and upcoming legal AI software are Harvey AI and Co Counsel. Both technologies state that they trained LLMs on legal data, and they are getting close to deployment (if only for the US market at this time). According to Clio , Harvey AI assists with contract analysis, due diligence, litigation, and regulatory compliance and can help generate insights, recommendations, and predictions based on data. CoCounsel reviews documents, prepares for a deposition, searches databases, creates research memos, and summarizes contracts and opinions. While much of this new phase of legal technology is shrouded in mystery, we do know for certain that a responsible lawyer and law firm must stay up-to-date with these developments. The use of AI in the legal profession is inevitable, and it will continue to transform the way legal services are delivered. Lawyers and law firms must embrace new technologies and find ways to incorporate them into their practice to stay competitive and provide better service to their clients. Enjoyed this article? Sign up and receive 500 points, gain access to our online booking tool, and receive notification of new articles delivered directly to your mailbox. At Andrew Roy Legal, we are committed to staying at the forefront of legal technology and innovation, and we are constantly exploring new ways to use AI to enhance our legal services. Our team of experienced lawyers combines legal expertise with cutting-edge technology to deliver bespoke legal solutions that meet the needs of our clients in a rapidly changing legal landscape. Whether you are an individual, a small business, or a large corporation, we are here to help you navigate complex legal issues and achieve your goals. Contact us today to learn more about how we can assist you. The information in this article is not legal advice and does not establish an attorney-client relationship. © 2023 Andrew Roy

If you enjoy this content, please consider signing up . Creating a member account is free, and you will · receive new content delivered directly to your inbox; · have exclusive access to members only content; · gain access to our online booking tool; · collect 500 bonus points in our points program. Regulation of AI The development and launch of Chat GPT has triggered an arms race in the field of artificial intelligence (AI), raising debates about the need for and methods of regulating AI. Recently, Elon Musk led a petition to pause the training of AI systems more powerful than GPT-4 for at least six months due to potential risks involved with AI. Governments worldwide are attempting to develop a regulatory framework, but with the rapid evolution of the industry, creating an effective regulatory framework has become challenging. For example, the EU's attempt to regulate AI has faced significant issues, including ChatGPT breaking the EU's plan to regulate AI . The Artificial Intelligence and Data Act The Canadian federal government aims to lead AI regulation and introduced Bill C-27, Part 3, the Artificial Intelligence and Data Act ( AIDA ) into the Canadian Federal Parliament on June 16, 2022. However, many commentators have identified serious concerns with the bill, including lack of consultation, exclusion of the public sector from the scope of the bill, the offloading of the most important work of the bill to its regulations, and an unclear enforcement model. Lack of Consultation The first and most significant issue with AIDA is the lack of meaningful consultation before its development and drafting. The Federal government failed to engage stakeholders in consultations before creating AIDA . Due to the complexity and ever-changing nature of AI, consultation is necessary to harness the vast potential of AI for Canadians while mitigating and addressing the risks and challenges associated with it. The lack of consultation will likely create a regulatory regime that will drive away investment from Canada while failing to protect Canadians Exclusion of the Public Sector The second issue with AIDA is the exclusion of public sector entities from the bill's scope, including government agencies, government departments, and federal political parties. This exclusion means that the Canadian federal government can design and develop AI systems that impact the entire Canadian population without oversight or regulatory approval. At a time when people are anxious and distrustful of government overreach into their privacy, this exclusion is concerning. The Federal government must maintain public trust by subjecting itself to the same regulatory regime as private industry. This concern is heightened because AIDA does not appoint an independent ombudsman for reporting, compliance, and enforcement. The Regulation Performs the "Heavy Lifting" The third issue with AIDA is the offloading of its most important "work" to its regulations. The bill creates a a regulatory system which is triggered by a "high impact system," but it does not define or provide guidance on the nature of a "high impact system." This raises serious questions, including whether a high impact system is a bright line test or judged on a sliding scale, how to measure impact, and how to differentiate high, medium, or low impact. Determining this definition and answering these questions are left to the regulations, which unfairly shortcuts meaningful debate in Parliament and casts a shadow of uncertainty on the scope and efficiency of AIDA . This uncertainty will negatively affect private industry's ability to prepare for this new regulatory regime. Onerous and Confusing Enforcement The final issue with AIDA is its enforcement model, including strict criminal penalties for non-compliance. Criminal offenses are mens rea offenses that include jail time. These strict penalties are combined with uncertainty regarding when one should notify and report. The definition of "material harm," another term mentioned but not defined in the bill, is left to the regulations, which leads to unanswered questions on what actually constitutes "material harm." This combination of strict penalties with an uncertain notification and reporting regime will lead to overreporting, translating into increased regulatory burden and costs, deterring AI start-ups from investing in the Canadian market. Scrap AIDA and Restart In light of these fatal problems with AIDA , the Federal government must scrap it and restart. The regulatory regime must be rethought and reworked, starting with meaningful consultation with stakeholders. The lack of consultation, the exclusion of public sector entities, the offloading of the bill's most important work to its regulations, and the unclear notification and reporting regime are detrimental to AIDA , which will drive away investment from Canadian markets, while providing dubious protection for the Canadian population. Enjoyed this article? Sign up and receive 500 points, gain access to our online booking tool, and receive notification of new articles delivered directly to your mailbox. Do you need bespoke advice on ensuring your company remains compliant with Canada’s privacy and technology regulatory regime? Andrew Roy Legal offers professional advice on all areas of intellectual property and technology law. Book a free consultation today! The information in this article is not legal advice and does not establish an attorney-client relationship. © 2023 Andrew Roy

If you enjoy this content, please consider signing up as a member . Creating a member account will ensure you will: receive new content delivered directly to your inbox; have exclusive access to members only content; gain access to our online booking tool; collect points in our points program. You have sent in an application for a trademark for your homemade microgreens business. You have received confirmation from the Trademark Office that it has passed the initial stage of the Trademark Examination Report. What follows next? The Advertisement Process After the Trademark Office (the "Office") completes the initial examination process, and if the Office is satisfied that the trademark is registerable, then the Office will perform a pre-publication search. If no confusing trademarks are found, an approval notice will be issued to you. You should review the approval notice carefully for accuracy and if any errors are found, then you should contact the Office as soon as possible. If there are no errors, then the Registrar of Trademarks ( the "Registrar") will proceed with advertising the application in the Trademarks Journal , which is a weekly publication issued by the Office. Regulation 41 The nuts and bolts of the advertising is covered in Regulation 41 of the Trademarks Regulations SOR/2018-227 . Regulation 41 requires that the application be advertised by publishing it on the website of the Canadian Intellectual Property Office with the following information: (a) the application number (b) the name and postal address of the applicant and of the applicant’s trademark agent, if any; (c) any representation or description of the trademark contained in the application; (d) if the trademark is in standard characters, a note to that effect; (e) if the trademark is a certification mark, a note to that effect; (f) the filing date of the application; (g) if the applicant filed a request for priority in accordance with paragraph 34(1)(b) of the Act, the filing date and country or office of filing of the application on which the request for priority is based; (h) the statement of the goods or services in association with which the trademark is used or proposed to be used, grouped according to the classes of the Nice Classification, each group being preceded by the number of the class of the Nice Classification to which that group of goods or services belongs and presented in the order of the classes of the Nice Classification; (i) any disclaimer made under section 35 of the Act; and (j) if the Registrar has restricted the registration to a defined territorial area in Canada under subsection 32(2) of the Act, a note to that effect. Paragraphs 41 (a) – (f) & (h) are self explanatory and are simply reproductions of what is already provided at the application for registration stage , and they do not require any more discussion. Paragraph 41 (g) Priority Paragraph 41 (g) refers to any claim for priority based on paragraph 34(1)(b) of the Trademarks Act , which states:
34 (1) Despite subsection 33(1), when an applicant files an application for the registration of a trademark in Canada after the applicant or the applicant’s predecessor in title has applied, in or for any country of the Union other than Canada, for the registration of the same or substantially the same trademark in association with the same kind of goods or services, the filing date of the application in or for the other country is deemed to be the filing date of the application in Canada and the applicant is entitled to priority in Canada accordingly despite any intervening use in Canada or making known in Canada or any intervening application or registration, if [...] (b) the applicant files a request for priority in the prescribed time and manner and informs the Registrar of the filing date and country or office of filing of the application on which the request is based; [...]
Subsection 34(1) (generally) and paragraph 34(1)(b) (specifically) allow for an applicant to claim priority of his trademark if he has filed an application for the same or substantially the same trademark for the same kinds of goods or services in another country of the Union within the last 6 months. Per the Trademarks Act , a “country of the Union” refers to (a) any country that is a member of the Union for the Protection of Industrial Property constituted under the Convention, or (b) any WTO Member; (pays de l’Union) Let us assume that you have also registered a trademark for your homemade microgreens business in the United States (because it is such a big market). You sent the registration for the trademark within the last 6 months to the USPTO. The United States is a Union country, so under subsection 34(1), you can claim priority based on that application date, rather than the date you filed the Canadian application. Of course, you would have indicated this earlier priority claim in your original application, and now at this stage, it would be advertised. Paragraph 41(i) Disclaimer Paragraph 41(i) refers to a disclaimer made under section 35 of the Trademarks Act: 35 The Registrar may require an applicant for registration of a trademark to disclaim the right to the exclusive use apart from the trademark of such portion of the trademark as is not independently registrable, but the disclaimer does not prejudice or affect the applicant’s rights then existing or thereafter arising in the disclaimed matter, nor does the disclaimer prejudice or affect the applicant’s right to registration on a subsequent application if the disclaimed matter has then become distinctive of the applicant’s goods or services. The history and treatment of disclaimers by the Registrar is interesting and potentially a subject of another blog post, but for our purposes here, we will just consider it in the context of the advertisement process. An applicant can voluntarily disclaim certain individual elements of a trademark because of their un-registrability in order to still salvage the trademark as a whole because the combination of elements are themselves distinctive. If you had made this disclaimer during the application process, then it would then be advertised with your application at this stage of the process. Paragraph 41(j) Geographical Restriction Regulation 41(j) refers to subsection 32(2) of the Trademarks Act : 32 (2) The Registrar shall, having regard to the evidence adduced, restrict the registration to the goods or services in association with which, and to the defined territorial area in Canada in which, the trademark is shown to be distinctive. Imagine that your trademark for your microgreens business was very similar to another company, which had been doing business for many years and was very popular, but only in Quebec. The Registrar may decide that your trademark is confusingly similar to this other Quebecois trademark and allow your registration to proceed on the condition that it does not apply in Quebec. If such a restriction applies to your application, then the details of this restriction would also be advertised. Withdrawal of the Advertisement Even though your trademark has made it to the advertisement stage, you are not yet out of the woods. The Registrar still has the authority to withdraw the advertisement under subsection 37(4). 37 (4) If, after the application has been advertised but before the trademark is registered, the Registrar is satisfied that the application should not have been advertised or was incorrectly advertised and the Registrar considers it reasonable to do so, the Registrar may withdraw the advertisement. If the Registrar withdraws the advertisement, the application is deemed never to have been advertised. The Trademark Examination Manual (the “Manual”) provides some guidance on when the Registrar may be satisfied that an application should not have been advertised: the applicant was not entitled to registration in view of a confusing pending application; an objection under section 12 of the Trademarks Act was overlooked or withdrawn in error (See 6.6) The Manual also provides some indication when the Registrar may be satisfied that an application was incorrectly advertised, and that would be in situations “where information, amendments or statements that were included in the latest application were omitted from the advertisement in error” (See 6.6). Next Steps Let us imagine that your advertisement was successfully published in the Journal and was not withdrawn by the Registrar. Per subsection 38(1) of the Trademarks Act , within two months after the advertisement of an application for the registration of a trademark, any person, on payment of the prescribed fee, may file a statement of opposition with the Registrar to oppose the registration by the applicant of the mark in question. Trademark opposition will be the next installment in our trademark series, so please consider signing up to ensure you receive the notification for our next article. Do you need to register your trademark? Andrew Roy is a Trademark Agent (Class 3) and a Trademark Lawyer that can help protect your rights. Call us today at 587.896.2769 or book a free, no obligation Zoom consultation The information in IP Iteration is not legal advice and does not establish an attorney-client relationship. © 2022 Andrew Roy