Predictive Analytics: Revolutionizing Legal Decision-Making with AI
Exploring the Benefits, Challenges, and Future of Predictive Analytics in the Legal Profession
Predictive analytics is a powerful tool that already in use in various fields, including the legal profession. Predictive analytics refers to the use of statistical algorithms and machine learning techniques to analyze data and predict future outcomes. The ability to accurately predict the outcome of a legal dispute would be a key tool in legal decision making for both law firms and corporate counsel. Predictive analytics can also be used to identify the likelihood of a legal issue arising in the first place.
One overlooked area of its use would be cases whose amount in dispute is so small, hiring a lawyer would not be cost effective. Imagine a landlord-tenant dispute over a $500 security deposit. Both parties may have claims to some or all of that deposit and, yet, neither would likely be willing to hire a lawyer to resolve the dispute. Submitting the matter to small claims has some attendant costs and time lost to attend hearings and work through things. At some point, a tool will emerge to serve this market by enabling both parties to essentially pay for a robot lawyer that can evaluate their respective claims and score their likelihood of success and provide a suggested settlement amount.
Predictive analytics can be used to identify patterns in case outcomes, which can help lawyers and judges make more informed decisions about how to handle a case. By analyzing data from past cases, predictive analytics can help lawyers identify the most effective arguments to make in court, and judges can make better-informed decisions about how to rule. ChatGPT already enables users to request the creation of content “in the voice of” some well known celebrity, political or historical thinker. Having 10-15 years of a judge’s opinions in a dataset, predictive analytics could provide insight into an anticipated future ruling by the judge. Knowing that likely outcome, it may well persuade parties to settle cases they otherwise might continue to fight out in court.
One example of how predictive analytics can be used in legal decision-making is in the area of contract law. Contracts are an essential part of the legal system, but they can be complex and time-consuming to draft and negotiate. Predictive analytics can be used to analyze data from past contracts to identify patterns and predict the likelihood of certain clauses being accepted or rejected. This can help lawyers draft more effective contracts and negotiate better terms for their clients.
Predictive analytics can be used to analyze data from past cases and identify the factors that are most likely to lead to a legal dispute. This can help businesses and individuals take proactive steps to avoid legal issues before they arise.
As with any new technology, especially one powered by AI, there are also some challenges to its use. One of the biggest challenges is ensuring that the data used to train predictive models is unbiased and representative of the given population. Bias in the data can lead to inaccurate predictions, which can have serious consequences in the legal context. As has already been exposed with ChatGPT, humans are often involved in the fine tuning of large language models (LLMs) influencing the answers such tools provide with their own human biases. Those biases can never be completely eliminated, but transparency with regard to data curation and modification is likely going to be a selling point for many AI tools of this type.
Another challenge is the need for transparency in the use of predictive analytics. It is important that lawyers, judges, and clients understand how predictive analytics is being used and what factors are being taken into account in making predictions.
Judicata is a tool, restricted to California law at this time, which offers predictive analytics in legal research. Its tagline is “Legal research, minus the lengthy search.” Its software purports to be able to read and analyze legal briefs evaluating their strengths or weaknesses, assigning a score for each brief based on the content. Here again, transparency about any potential biases in the LLM or algorithm would be key for customers wanting to ensure that the output they receive is as objective as possible.
There remains a great unknown when it comes to judges using such tools. Imagine an AI predictive analytics tool relied upon by judges to help them decide cases. Such a tool would wield significant power in the legal system and potentially do so without the judge being aware of how the tools responses are influencing those decisions.
The Resistance Will Fade
The legal industry has a reputation of being slow to change. But, the public, the client facing part of the industry, will continue to demand cost-effective representation. They will also be increasingly willing to experiment with robot lawyer options enabling them to bypass hiring lawyers altogether. Tools not yet even invented will have the opportunity to democratize legal services, redefine a lawyer’s role in the legal system and transform it from outside.
Tools like Legal Zoom already enable people to essentially draft their own documents, obtain legal guidance without lawyers, and evaluate their legal risks. That is laying the groundwork for a more equitable legal system. While progress may be slow, the path ahead is clear.
Outside of the Law
While we here at Legal AI are focused on the legal application of various AI tools, keeping an eye on what is happening outside the law is often a good insight into what is coming. The capabilities of GPT-3 and its descendants will be (and are) diverse and remarkable, encompassioong programming and debugging, musical composition, student essay writing and grading (which has raised concerns among academic institutions), and even poetry creation. In fact, previous applications of an earlier version of GPT by scientists have resulted in the generation of novel protein sequences, highlighting the limitless potential of this technology. It's important to note that these are just some examples of what GPT can do, and there is still much to be explored and developed.