Augmenting the Legal Profession
While AI can give us comprehensive models that no human has come up with, and it can continue to learn based on new input, it does have its limitations. A great example of one such limitation, illustrated by Richard Zuroff, Senior Manager of Industry Solutions at Element AI, is in deciphering the meaning of seemingly simple sentences. In the phrase: “The trophy does not fit in the suitcase because it is too big”, any person would naturally draw the conclusion that the trophy is the object being referred to as “too big”. But, a simple switch to “The trophy does not fit in the suitcase because it is too small” triggers a different understanding. The human mind understands that the suitcase is the object being referred to as “too small” because the mind can draw on experience and context. However, this simple switch is still something that stumps AI, despite how advanced the technology has become in many areas. Zuroff attributes this to a lack of real world understanding by AI - he says pattern matching, which is what AI does, does not equate to logical reasoning.

So AI is not perfect, and neither is the human mind. Zuroff believes those gaps may lead to a perfect partnership. He supports an augmentation approach to the legal profession, rather than an automation approach. AI is currently able to create transcripts from recorded interviews or testimony, create summaries of case histories, and even make suggestions regarding the best sources to use for research. Lawyers can then apply this efficiently accumulated knowledge, using context and life experience to come up with prudent legal advice or to help craft an argument. With this augmentation approach, Zuroff believes large firms can become more efficient, while small firms can gain the capabilities of a much larger firm.

The quality of any given AI technology is highly dependent on good AI design. Zuroff emphasized that however AI is designed, AI technology needs to be tailored to a specific industry and predicted use.

As we move into an age where AI will almost certainly play a bigger role than it already does, Zuroff had a few suggestions. He said that investing in complements to the new technology is critical. We need infrastructure within firms to support the new AI strategies, and a willingness to depart from the typical workflow. We also need governance and oversight over AI solutions. Without oversight, risks of implementing AI may include , , and having the AI .

Litigating AI
Criminal defence lawyer, Jill Presser stressed the need for good regulation of AI, which could prevent the need for litigation in this area. To Presser, good regulation means that AI outputs should be reviewable for reliability, and their often proprietary codes need to be subject to orders of disclosure in court. She also flagged the need to determine the admissibility of expert evidence of those who understand the decision-making processes of AI.

One other issue that triggers the need for strict regulation is automation bias, as demonstrated in , a case in which an actuarial tool assessed a Metis man’s psychopathy and risk of recidivism and placed him in maximum security prison. The tool was developed and tested on non-Indigenous populations, and no research confirmed the results would be valid when applied to Indigenous persons. Presser says that with adequate regulation, the burden of proving that these AI tools are well tested and reliable would be placed on the party seeking to use those tools. Regardless of regulations, however, Presser believes AI tools should not be determinative, and should only be helpful aids in sentencing and other decisions. Read more about issues with bias and transparency in AI policing tools , and more about the issues with governments using and regulating AI .

Immigration and AI
Patrick McEvenue, Director of Digital Policy at Immigration, Refugees and Citizenship Canada (IRCC), explained that IRCC is currently trying to determine how AI can be used in the application review process. Right now, they are looking at their back catalogue of applications to mark straightforward applications that were approved. They then feed those to the AI program, which learns and makes decisions about new applications. Applications that are not initially approved by the program are brought to an officer for review, and the officer’s ultimate decision feeds back into the system to help it learn. McEvenue stressed that the process is very human-centred, and the AI component of the process follows the to ensure transparency and procedural fairness. So far IRCC is only using AI to approve applications, not deny them. McEvenue anticipates that a lot of work will need to be done and a lot of questions answered by the IRCC before it can start using AI to deny applications. You can read more about the concerns of using AI in immigration .

AI and ‘Smart Contracts’
The final panelist was Amy ter Haar, a lawyer and doctorate of law candidate. Her area of interest is smart contracts, which may have multiple uses in the near future, including banks giving out loans or facilitating automatic payments, insurance companies processing claims, and even postal services enabling payment upon delivery. explains smart contracts pretty well, but if you don’t have time, here’s the basic rundown: A smart contract is a piece of code stored in a blockchain. In the video, the example is Kickstarter, the popular crowd-sourcing website. If a company chooses to find supporters through Kickstarter, both the supporters and company must put their trust in Kickstarter as the intermediary who holds their money and either grants it to the company once the fundraising goal is reached, or refunds it to the supporters if the goal is not reached. In contrast, if a smart contract is used, the contract holds the supporters’ money in escrow until the final date to reach the fundraising goal arrives. At that point, it automatically allocates the funds to the appropriate party. The smart contract removes the third party (Kickstarter) from the process, and therefore removes any trust concerns or delays. It never allows total control over the money by any party. Smart contracts are also immutable, so once made they can never be changed. Some say this is a benefit, in that contracts cannot be tampered with, while others are concerned about the inability to correct or edit the contract. See for one critic’s concerns.

Other issues smart contracts may raise is that parties may enter into them with a pseudonym. This may pose issues regarding the legal capacity to enter into a contract. In addition, there is a question as to where and how contracts could be enforced, if neither party discloses their location. See for a more comprehensive understanding of the concerns.

Regulation, regulation, regulation
Amongst all the interesting information conveyed at the conference, the one sentiment that was repeated throughout the day was that there is a real need for the regulation of AI, and soon. Tough questions posed by Carole Piovesan, a co-founder and partner at INQ Data Law, such as - who is liable for the outcomes of AI decisions? What happens when there is a , for example? Or, when ? – underscore this need. While these tough questions need answers, they do create an exciting and interesting space in the legal profession. Those interested in taking up the challenge will likely discover that regulation has to be industry-specific, and it must take into account potential biases, tendencies towards collusion, privacy concerns, enforceability, and transparency, amongst other factors.

Written by Rachel Marcus, IPilogue Editor and JD Candidate at Osgoode Hall Law School.

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Artificial intelligence (AI), although lacking a universal definition, can be understood as a set of related technologies running on algorithms that replace or augment human cognition. With the growing capacity of AI, we find ourselves in Klaus Schwab’s “” where human brains remain important but are not the only source of intelligence. My article examines the risks and benefits of giving AI programs a role within rights-determining administrative bodies (i.e. tribunals, also called commissions or boards). Specifically, I propose statutory authorization for what I am calling “AI-Supported Adjudication” (ASA), an example of the “” (where the aptitudes of AI and humans are combined). My basic contention is that while neither humans nor AIs are perfect, pairing them is the most desirable future model for tribunal adjudication. The two other options are (1) maintaining the status quo, and (2) full AI automation. With new technologies come novel risks, but also new affordances. The growing capabilities of AI should inspire us to think beyond the status quo and consider alternative models of adjudication and decision-making. At the same time, the importance of uniquely human qualities and the hazards of unsupervised AI leads me to reject the radical notion of full-fledged “”.

AI tools could help ensure individual adjudicators consider all relevant materials. They would aid with research, for example flagging pertinent sources, highlighting specific excerpts, and assigning relevance ratings. On an institutional level, they could track and evaluate decision-making patterns across a tribunal. Tribunal AI is hypothetical, but a realistic prospect given that AI has been deployed in similar contexts like and by government agencies of .  These experiments in AI-assisted justice and governance have drawn controversy (see the Harvard Law Review’s piece on and The Guardian’s coverage of ). Is it possible to leverage AI’s benefits while avoiding the missteps made elsewhere? The Canadian government thinks so, as evidenced by the Treasury Board’s recent “” from May 2018. This Treasury Board document solicits private sector proposals for AI solutions to be deployed across several government entities, including the Immigration Ministry and the Department of Justice (for a human rights report on using AI in Canadian immigration see “”).

While in theory ASA will be greater than the sum of its parts, there are challenges to address. There are issues of judicial review and lawful delegation that I explore in my article.

Also, when humans make decisions with AI tools there is a risk of overreliance. Even if the human is “in charge” there are psychological tendencies that might reverse this relationship in practice. “” and “” are two examples. In a tribunal context, this could lead to “adjudication by algorithm”, illegal fettering of the adjudicator’s discretion, and de-skilling of adjudicators. Procedural fairness is another issue (what Americans call “due process”). Decisions of a tribunal must be reasonably transparent and unbiased. Some say AI is not sufficiently transparent, as we cannot know how it reaches its conclusions (the so-called “” problem). Others believe that through technical solutions (i.e. “”) we will eventually “open up the hood” of AI to see the basis of its decisions. It is worth noting the problem of opacity also exists with human thinking. It is cautioned in administrative law, for example, that a requirement to provide written “reasons” does not mean those reasons will be the true antecedents of the judgment. A second aspect of procedural fairness, an unbiased hearing, may pose greater difficulties. There are many ways bias can become encoded in algorithms, and once there, it may be difficult to detect or trace its source. However, again we must evaluate the human comparator. Human thinking is subject to numerous biases and shortcomings. In the context of adjudication, it was said that ‘‘Every judge...unavoidably has many idiosyncratic learnings of the mind...which may interfere with his .” Contemporary research shows how [2] like whether an adjudicator has eaten lunch can influence the harshness or leniency of a decision.

AI is not perfect, but neither (if we’re being honest) are human adjudicators. As poetically notes, we are all “in our own natures frail, and capable of our flesh; few are angels.” I suspect one of the greatest challenges of our era will be wading through the #legaltech hype and the doomsday prophesizing, and rationally taking honest stock of AIs strengths and weaknesses, as well as our own. As noted by of U of T Law, advances in computational power and sophistication mean that ‘‘the set of tasks and activities in which humans are strictly superior to computers is becoming vanishingly small”. This does not mean that computers or AI are superior, only that a humanity would be remiss not to utilize the power it has in these tools. The search for the ideal adjudicator, spurred on by AI’s advances, will likely settle on an approach that engages with humanness, in all our virtues and shortcomings. To err is human, so why not augment with AI?

Jesse Beatson is a JD Candidate at Osgoode Hall Law School. 

[1] Jesse Beatson, “AI-Supported Adjudicators: Should Artificial Intelligence Have a Role in Tribunal Adjudication?” (2018) 31:3 Cdn. Journal of Admin. Law & Practice 307.

[2] Craig E. Jones, “The Troubling New Science of Legal Persuasion: Heuristics and Biases in Judicial Decision-Making” (2013) 41  Advocates' Quarterly 49.

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Margaret Hagan’s work with the at Stanford University is a case in point – dedicated to “marrying legal expertise with design thinking to help people better understand the law and improve access to justice,” the project has already created more than 10 apps, including Law School Dojo and GRE Dojo, which use quizzes to help users gain a better grasp of the law and the GRE respectively. Her work is dedicated to fusing legal and design expertise to transform how people may access legal information, advice and dispute resolution.. Last Fall, Margaret collaborated with Professor Pina D’Agostino at Osgoode’s Intellectual Property Centre, IP Osgoode, and colleagues at 91��ɫ’s Lassonde School of Engineering to launch an dedicated to new ideas in Patent Law.

Osgoode’s newest class, , exposes students to lawyers working at the forefront of legal tech in contexts of new legal services and new forms of dispute resolution. The course is taught by Monica Goyal, Nicole Aylwin and Darin Thompson, three leading innovators attempting to bring design principles to the integration of technology in access to justice solutions. In these contexts, students are asked not just to explore design ideas and frameworks, but to design their own tech solution to justice problem. As Nicole Aylwin of Osgoode’s Winkler Institute for Dispute Resolution, which hosted the final student presentations in the course this past year noted: “Innovation requires new ideas, energy, passion, and sometimes it requires a different perspective than one is used to. Students bring all of that, they bring energy, great ideas, a different way of looking at the system. They have not been in the system for very long, and it is terrific to see them thinking not only about justice issues, but about related fields about technology and design and applying that to justice innovation issues.”

Another ambitious step toward a future of justice system design is the which has been created (and curated) by the . The JDP is a week-long workshop for post-secondary students interested in law, design and access to justice. Participants will explore the justice system from the “user” perspective and approach access to justice as a design challenge – gather the information needed on how the justice system works, imagine how best to respond to the gaps and weaknesses of the current system, and develop a prototype that will improve it. By the end of the JDP, participants will have generated their own response to the challenges of access to justice – whether a new way of providing legal information through social media, or new ways of combining legal, social work and medical services in problem-solving clinics, or new ways of bringing courthouses to communities or imaginative approaches we have not even considered yet.

The user focused, design approach is particularly appropriate for the justice system because so much of the system we now have appears designed with the needs of lawyers in mind rather than the needs of the public. The JDP is also a dynamic setting in which to expand the reach of legal thinking beyond those already in Law School and committed to a particular lens on legal practice. As new voices and perspectives are added to the mix, the design choices and possibilities expand.

I can’t wait to see the ideas that the JDP participants dream up, to engage with Osgoode’s other related initiatives, and most of all to see the impact of a new generation that will come to see the justice system as a design in progress.

The original post from Dean Sossin's Blog is available .

Lorne Sossin is the Dean of Osgoode Hall Law School and an IP Osgoode advisory board member.

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