AI and the Law

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Updated November 2, 2022

You’re reading an excerpt of Making Things Think: How AI and Deep Learning Power the Products We Use, by Giuliano Giacaglia. Purchase the book to support the author and the ad-free Holloway reading experience. You get instant digital access, plus future updates.

Justice cannot be for one side alone, but must be for both.Eleanor Roosevelt

Law seems like a field unlikely to make use of artificial intelligence, but that is far from the truth. In this chapter, I want to show how machine learning impacts the most unlikely of fields. Judicata creates tools to help attorneys draft legal briefs and be more likely to win their cases.

Judges presiding over court cases should rule fairly in disputes for plaintiffs and defendants. A California study, however, showed that judges have a pro-prosecutor bias, meaning they typically rule in favor of the plaintiff. But no two people are equal, and that is, of course, true of judges.* While this bias is a general rule, it is not necessarily true of judges individually.

For example, let’s use California Justices Paul Halvonik and Charles Poochigian to show how different judges are. Justice Halvonik was six times more likely to decide in favor of an appellant than Justice Poochigian. This might be surprising, but it is more understandable given their backgrounds.

Justice Halvonik, California’s first state public defender, was slated for the state Supreme Court. Unfortunately, a drug charge for possessing 300 marijuana plants curtailed that dream and ended his judicial career. Justice Poochigian, on the other hand, was a Republican State Assemblyman from 1994 to 1998. Republican Governor Arnold Schwarzenegger appointed him to the California Courts of Appeal in 2009.

While we should not boil their behavior down to stereotypes, we can look at the facts of each of their rulings and any trends that may develop from them. To do this, we must examine the context of the type of case and the procedural posture, meaning how similar cases were ruled on before.*

Judicata

Judicata, a startup focused on using artificial intelligence to help lawyers, identifies statistics for each judge and uses those to see how a judge is likely to rule on a case. It takes into account their rulings based on the plaintiff or defendant and provides a glimpse of what other aspects might change what the judge will do, like the cause of action or appeal.

Judicata’s application, Clerk, was the first software to read and analyze legal briefs, which are written legal documents used in a court to present why one party should win against another.* Clerk’s purpose was to increase lawyers’ chances of winning a motion, that is, to win a request for the judge to decide the case.

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Figure: An example of a score that Clerk generates.

  1. “Relying on strategic and favorable arguments.”

  2. “Reinforcing those arguments with good drafting.”

  3. “Presenting the context in which the brief arises in a favorable way.”*

Analyzing Arguments

The lawyer’s execution on these three dimensions can be judged by an objective measure, such as:

  1. “Winning briefs perform better than losing briefs along each of these dimensions.”

  2. “Higher scoring briefs have a better chance of winning compared to lower scoring briefs.”

The ability to grade a brief is crucial because whatever you can measure, you can improve. Given a brief, Judicata’s program analyzes arguments inside the legal brief and evaluates them, based on whether it contains logically favorable arguments. It analyzes all legal cases, legal principles, and arguments cited in the document and determines which ones are most prone to being attacked based on previous data.

Figure: Analysis of different arguments used.

Based on that information, it creates a snapshot of which arguments were used in which contexts. Some arguments are used for the defendant and others for the plaintiff, the party that initiated the lawsuit.

Figure: Cases that reference similar arguments as this brief.

Surprisingly enough, relying on arguments that were previously used on the same side as the lawyer works better. So, if you are a lawyer defending a case, it is better to use arguments that were used on the defendant’s side. Clerk also suggests arguments that have historically worked well for the party in question. Clerk benefits lawyers who want to create favorable and stronger arguments.

Figure: Suggestions of cases that can help the lawyer win the brief.

Improving Briefs

Whenever a lawyer writes a legal brief, it needs to include precedents, previous cases that support their case. Judicata found that the best cases to include were ones that matched the same desired outcomes that the brief is trying to achieve. Clerk analyzes previous legal cases and suggests precedents that were used in winning cases, identifying better cases to support the brief. The goal is to help lawyers present better drafted briefs.

Figure: Analysis of the draft.

Preparing Fair and Balanced Cases

Lawyers not only have to present good arguments and precedents, but they also need to address the opposition’s side. Clerk discovers how many arguments and precedents need to be addressed on both sides and suggests ones to add or remove. With that, lawyers present a stronger and more fair and balanced legal case.

Figure: Analysis of the arguments used by the opponent side.

Analyzing the Context of the Case

Finally, Clerk analyzes what the outcome might be for a certain judge. Different judges analyze cases differently. So, depending on their historical decisions, Clerk gives a probability that the brief will succeed in each of the possible scenarios.

Figure: Probability of how a side of the case might win the case.

Even if the context a lawyer finds themselves in is not favorable, that does not mean all hope is lost. The lawyer merely needs to find historical cases that tilt this trend in their favor. And even if the lawyer does not have more than a 50% chance of winning the case, the ruling may still go in their favor. With Clerk, lawyers can better argue their case. Justice is said to be blind, but when it is not, machine learning can help lawyers make their case.

AI and Real Estate

It amazes me how people are often more willing to act based on little or no data than to use data that is a challenge to assemble.Robert Shiller*

Homes are the most expensive possession the average American has, but they are also the hardest to trade.* It is difficult to sell a house in a hurry when someone needs the cash, but machine learning could help solve that. Keith Rabois, a tech veteran who served in executive roles at PayPal, LinkedIn, and Square, founded Opendoor to solve this problem. His premise is that hundreds of thousands of Americans value the certainty of a sale over obtaining the highest price. Opendoor charges a higher fee than a traditional real estate agent, but in return, it provides offers for houses extremely quickly. Opendoor’s motto is, “Get an offer on your home with the press of a button.”

Opendoor buys a home, fixes issues recommended by inspectors, and tries to sell it for a small profit.* To succeed, Opendoor must accurately and quickly price the homes it buys. If Opendoor prices the home too low, the sellers have no incentive to sell their house through the platform. If it prices the home too high, then it might lose money when selling the house. Opendoor needs to find the fair market price for each home.

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