Impartiality is often associated with a neutral, impersonal point of view, or an observer that is hypothetically free of subjective biases. The earliest proponents of these views were David Hume () and Adam Smith (). One dimension of impartiality is the concept of being impersonal, meaning dispassionate or indifferent. For instance, the good judge is impartial insofar as they are not swayed by emotions and do not factor in personal considerations. An angry judge should not deliver a harsher sentence to a defendant, nor should the judge deliver a more lenient sentence because the judge and defendant both enjoy jazz music.

Another related concept held up as a virtue for a judge is “neutrality.” (with the help of ) can help us understand neutrality by the distinction between the concepts of agent-relative and agent-neutral. The basic idea is that a reason for action is agent-relative if it makes some essential reference to a person, and it is agent-neutral if it does not. If I were a judge, I would act on agent-relative reasons if I delivered a harsher sentence because the defendant angered me (since my anger is a reason for me but nobody else). In this case, acting agent-neutrally is to act in a way in which agent-relative reasons are yet to be specified. The relationship between neutrality and impartiality is that neutrality is a necessary condition to impartiality, but neutrality on its own denotes a narrower idea of non-specificity.

Algorithms can be perfectly neutral because they are not subject to emotions or other physiological limits. suggests that algorithms can be used for sentencing in order to combat concerns of judicial arbitrariness and bias.  The results could lead to greater justice by getting a bit closer to the ideal of proportionality in sentencing. That is, even if the algorithm is not perfect, it would do better than judges, especially with respect to racial bias. attempted something like this in the 1970s and 1980s, and it largely failed because judges trusted their own judicial discretion and intuitions over these algorithms.  While there are legitimate concerns with introducing novel technologies, technophobia should not be an impediment to a more just legal system.

Still, the concerns related to taking the human element out of judgments have some substance. Leaving aside issues around implementation, one may wonder how impartial reasoning squares with . For instance, in morality, impartial reasoning is not always appropriate. In 1793, William Godwin imagines a scenario where one must choose to either save a chambermaid or Fenelon (the archbishop of Cambrai) from a fire. From an impartial standpoint, the clear outcome would be saving Fenelon, since he benefits thousands with his works. Even if the chambermaid was one’s own wife or mother, the choice would be the archbishop. This may seem like a morally repugnant result. Indeed, feminist ethics teaches us about the importance of and in morality.

While there are a number of issues around implementing algorithms to assist the judiciary, there is clear potential for addressing access to justice issues. For example, predictable sentencing outcomes could level the playing field in negotiations between the Crown and the accused, increase efficiency for judges, and assist lawyers in building a case. is already involved in “AI-powered platforms accurately predict court outcomes and enable you to find relevant cases faster than ever before.” With already beginning at the Supreme Court of Canada, I am optimistic about the next steps in operationalizing legal technology.

Written by Dan Choi, a second year JD Candidate at Osgoode Hall Law School and an IPilogue Contributing Editor.

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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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The day began with Aron Solomon and Jason Moyse of LegalX welcoming everyone to the event and discussing what we, as attendees, could expect from the forum. During the introduction, Marvin Minsky, a Professor from MIT, was quoted for saying “you don’t understand anything until you learn it more than one way” – a quote which I believe accurately summarizes the discussions of the forum.

The quote is particularly suitable as much of the forum focused on the stagnant nature of the legal field. In other words, because the legal market is operating in a precedential, non-evolutionary scheme, without a focus on how technology can be used to improve it, those currently directing the legal field “don’t understand it” because they have only learned it the traditional way. As such, the forum discussions focused on how the implementation of technology – a new way of learning – could solve many of the issues arising in the legal field.

, Associate Professor of Law at Illinois Institute of Technology Chicago-Kent College of Law, was the keynote speaker and discussed how we might re-make the legal profession through an understanding of risk. In this discussion, Katz stated that a great lawyer will price risk, and in order to accurately price risk one must be able to predict outcomes. Being able to price risk makes for a great lawyer, as a proper prediction of risk will lower legal fees for clients. To explain this concept, Katz gave the example of a lawyer spending multiple hours amending a single clause of a contract which may cost a client, say, $1,000 in labour fees. However, if one were to determine the “price” of the risk associated with a contract without the amended clause, one might realize that the maximum liability for the client is $100. As such, a great lawyer would properly price the risk, and conclude that it would be in the best interest of the client to not amend the clause.

Accordingly, Katz argued that to revolutionize law we need to be able to predict outcomes in order to price risk, and to do so we need to keep the artisan features of the law, but add scientific functions. There are three ways to predict any outcome – experts, crowds, and algorithms – and the combination of the three will always outperform an independent prediction method.

Further examples of how technology can be used to improve the legal market were then discussed by expert-filled panels from all areas of the legal profession. The panels provided a balanced, in-depth, and at times passionate, debate about topics such as contract automation, design thinking, and data analytics. From each panel, the attendees were exposed to examples of technologies that have either succeeded or failed in the legal market, as well as insights into the technological desires of progressive law firms and clients.

Although the topics discussed by each panel were centred on various forms of technology which could improve the legal field, many of the questions posed by the audience focused on the implementation and application of such technologies. It seemed that many attendees saw the potential of the technology, but could not foresee what would influence the legal market to adopt these technologies.

However, any doubts were soon erased by Fred Headon, Assistant General Counsel at Air Canada. During the contract automation panel, Headon straightforwardly stated that clients will force law firms to evolve. He stated that a company like Air Canada will expect a law firm to be able to implement the same technology they do. Furthermore, he added that a client that can make airplanes fly would not understand an inability to automate a contract, and such a client would therefore find a law firm willing and able to cut costs accordingly.

Overall, the Emerging Legal Technology Forum was an amazing event full of insightful ideas about how we might see the legal market change in the years to come. However, it was not only the speakers who made this forum such a success. The lunch break and reception were full of lively discussions on the topics of the day. Furthermore, many of the attendees took advantage of the event’s by sharing their thoughts of the event with the hashtag #trlegalx.

Denver Bandstra is a JD Candidate at Osgoode Hall Law School.

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Tell me a bit about Lex Machina. Where did the idea for Lex Machina come from and what has it turned into since the early days of the company?

Lex Machina began as a public interest project at Stanford Law School. A number of big law firms and technology companies donated a couple of million dollars to what was then called the “IP Litigation Clearing House,” which Professor Mark Lemley spearheaded to create the first electronic set of patent litigation analytics and outcomes. After the money was donated and was turned into a public interest project Lex Machina spun out of a private venture in 2010, as so often happens at Stanford when someone says, “Hey I think there might be a business here”.

Unlike traditional legal research that requires top-down control, Lex Machina provides a novel legal resource called legal analytics. We mine litigation data and then clean, code, tag, and build meta-datasets. This data then enables lawyers to make data-driven decisions in their strategy and tactics that they employ for their cases, transactions, and even for obtaining new clients.

Legal technology has been slow to develop relative to technological advancements in other industries. Why do you think this is?

The legal services industry organizes itself around precedent and so by nature change is slow. Practicing law is a very old profession and has been done the same way for a long time. Personally, I think that the emergence of legal analytics is as big a moment and analogous to the moment when legal research switched from books to computers.

I like to use the “Moneyball” analogy. Analytics were applied to the Oakland A’s and transformed their ability to win even though they had a smaller market and payroll. Now, every team employs Moneyball analytics. The same is true for law as we are able to turn lawyers into Moneyball lawyers. You still need great legal research and legal reasoning skills but analytics supplements those skills. For example, let’s say that you are a patent lawyer and your client is suing in Delaware for patent infringement. As a lawyer you determine that you will have a better chance at winning the case using a jury in Chicago. Typically, you would do some legal research and use your best legal reasoning to persuade the judge that there is a basis for transferring your client’s case to Chicago. A Moneyball lawyer is going to do all that but they will also go into Lex Machina to unpack how that judge has previously treated motions to transfer in patent cases and extract from the data where that judge’s preferences lie. Everyone, including judges, have subconscious biases and we at Lex Machina know to the second decimal point how judges behave. In our example, if the case in Delaware had been assigned to Judge Robinson and you need to persuade her to move the case to Chicago, you can go into Lex Machina, push a button, and find the 20 patent cases that have succeeded in motions to transfer under Judge Robinson. You can then use that data to uncover how best to approach your argument for a motion to transfer. The data allows you can determine how to speak to Judge Robinson in terms that are more favourable to her, thereby increasing your odds of winning.

Companies such as LexisNexis and Thomson Reuters have been developing legal technology for a long time. What can a start-up culture for legal technology offer that customers might not be able to get from more mainstream legal technology companies?

Just like any industry, big incumbents get big by doing something well. Both LexisNexis and Thomson Reuters provide excellent online legal research environments. They are able to structure their information similar to a law library and train lawyers how to perform legal research using their technology. This strategy has allowed them to become very good at what they do. As startups, however, we are not burdened with that history. We can be nimble and provide services that haven’t yet been done, and try to deliver those services in a saleable market so that we have a successful venture. The rise of e-discovery and the use of algorithms to identify which documents in discovery are relevant to a case was largely driven by small startups. There are also companies like , , and that are now doing analytics around billing data. These services all developed through startups that took a fresh look at existing needs and came up with answers for those needs that had not yet been proposed by the big incumbents. Therefore, just like any industry, there is going to be transformation that is driven by startups who are not afraid to tackle big problems with new approaches.

What is the long-term goal for Lex Machina? Where do you see it in 10 or 20 years? 

We want to bring legal analytics to the law. We have had a great run with our beachhead market of patent litigation. In some respects that was because of luck. If the project at Stanford from which we grew was in a different area of law, it is not clear that it would have provided the same platform for transition into a private sector venture. We have also provided access to data about copyright, trademark, and antitrust, and are now building the Robust Analysts tools for copyright and trademark, which is a platform that we currently provide for patent litigators.

Aside from our IP analytics we have also studied another half a dozen federal subjects. Our focus on federal subjects is largely because we collect our data using Pacer, the federal court reporting system. Whether it is tax, securities, employment law, or another federal subject, we hope next year to move past IP by providing legal analytics to the rest of the federal subjects. In 10 or 20 years, I hope that we are providing analytics for every subject in law, including state law subjects, and that every attorney has his or her specialty empowered with analytics. While there will probably still be those big research incumbents, we hope that there will also be a big legal analytic incumbent called Lex Machina.

Thanks again for meeting with me today, Owen. Is there anything else you would like to add?

Considering that many of your readers are in academia I'd like to make two more points. First, because of our Stanford heritage, we still have a large public interest component to our business. We provide free access to our platform to members of Congress and their staff, to the federal judiciary and their clerks, and to academics and students who are studying IP. We are here to do good and do well, and our public interest program helps us fulfill part of our mission.

Second, I find it interesting that the information about trends in patent litigation is of great interest to academics and journalists. We are glad that we can provide that sort of information. However, we are going to succeed as a private venture not by looking at those trends but by allowing lawyers to make data-driven decisions and to predict what sort of approaches will affect outcomes and strategies in cases. While trends get a lot of attention in articles, it is the application of analytics and its data to the day-to-day practice of law that holds the most hope in transforming and improving the practice.

Peter Neufeld is the Features Editor of the IPilogue and a J.D. Candidate at Osgoode Hall Law School. Peter met with Owen Byrd when he was a Visiting Researcher at Stanford Law School's Codex: The Stanford Center for Legal Informatics as part of Osgoode's Intellectual Property Law Intensive Program.

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