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Voice of Experience

Voice of Experience: October 2024

Using AI for Predictive Analytics in Litigation

Ashley Hallene and Jeffrey M Allen

Summary

  • Learn how AI-powered predictive analytics can forecast litigation outcomes, guide strategy, and offer insights into judicial behavior.
  • See examples of ways lawyers can apply predictive analytics to various stages of a matter. 
Using AI for Predictive Analytics in Litigation
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Picture this: your law firm is involved in a high-stakes contract dispute, and you are facing a critical decision: settle for millions or risk going to trial. Traditional legal intuition leaned towards settling, but an AI-powered predictive tool told a different story. By analyzing hundreds of similar cases, judge profiles, and court rulings, the AI predicted an 80% chance of winning if they took it to court. Armed with this insight, the firm confidently pursued litigation—and won. This case wasn’t a gamble; it was a calculated risk made possible by AI, highlighting how predictive analytics is revolutionizing legal strategy. You have likely heard the saying, “If you can’t know the law, know the judge.” This phrase emphasizes the strategic advantage of being familiar with how a particular judge interprets and applies the law, as judges can exercise significant discretion in legal decisions. In the context of AI, predictive analytics tools help lawyers "know the judge" by analyzing past rulings and behavioral patterns, offering valuable insights for litigation strategy.

Predictive analytics involves using AI and machine learning algorithms to analyze large datasets, including past case rulings, legal filings, judicial decisions, and even jury behavior. The goal is to predict future legal outcomes and guide strategy by identifying patterns and trends. AI platforms aggregate vast amounts of legal data, including case law, statutes, court rulings, and historical outcomes. These datasets can include both structured (e.g., case records) and unstructured data (e.g., written opinions). Algorithms can be applied to detect patterns in the data, such as how certain judges rule on specific types of cases or which arguments have historically succeeded in certain courts. Through this analysis, AI tools can predict the likely outcome of a case, considering factors like jurisdiction, judge behavior, precedent, and case specifics. This can include forecasts on whether a case is likely to settle, win, or lose. This can help you evaluate the risks associated with proceeding to trial versus settling. It can highlight potential weaknesses in a case and provide insights on likely timelines and costs. Let’s examine how you might use predictive analytics in your practice:

Initial Case Evaluation

Hopefully, you have gathered enough information in your initial consultation for a thoughtful review of the matter. Using that information, predictive analytics tools can then analyze historical data from similar cases to predict the likely outcome of the new case. This includes assessing the probability of winning or losing, potential settlement amounts, and the duration of the case. This helps the lawyer provide the client with a realistic expectation of the case’s prospects. It can also help the lawyer allocate resources efficiently, including determining the amount of time, personnel, and budget required to handle the case effectively.

Evolving Case Strategy

If the matter progresses and you have identified both the judge for your matter and opposing counsel, then predictive analytics can offer further insights into their past rulings and strategies that may shape your strategy. For example, let’s say a lawyer is preparing to represent a client in a complex business contract dispute. The case has been assigned to Judge Smith, who has a reputation for favoring strict interpretations of contract law. The opposing counsel, Attorney Johnson, is known for aggressive litigation tactics and frequently pushes for early settlements.

Using a predictive analytics tool, the lawyer examines Judge Smith’s past rulings in similar contract cases. The tool reveals that Judge Smith consistently sides with plaintiffs in cases where the contract language is ambiguous but favors defendants when the contract is clear and unambiguous. Additionally, the data shows that Judge Smith tends to issue summary judgments in cases with well-documented evidence, avoiding lengthy trials.

The lawyer then uses predictive analytics to analyze Attorney Johnson’s litigation history. The data reveals that Johnson tends to propose settlement offers early in the litigation process but pushes cases to trial when facing weaker evidence. The tool also shows that Johnson has a pattern of using procedural motions to delay proceedings, often aimed at pushing the other party into settling out of frustration.

Armed with this information, how might the lawyer adjust his strategy? Based on Judge Smith’s tendency to issue summary judgments, the lawyer decides to present a case with clear, well-documented evidence and highlights the contract’s explicit terms. This strategy increases the likelihood of a favorable summary judgment, potentially avoiding a drawn-out trial. Since the lawyer is also armed with the knowledge of Attorney Johnson’s usual tactics, the lawyer prepares to counter early settlement offers and procedural delay tactics. The lawyer also takes steps to expedite the filing process and remains firm on pursuing a trial, knowing that Johnson often pushes weaker cases to trial if procedural delays succeed.

Which Way Forward? Settlement vs. Trial Analysis

Another useful facet of predictive analytics is its ability to identify key factors that influence settlement outcomes, such as the nature of the dispute, the jurisdiction, the judge, and the opposing counsel. Here is another example to consider. Let’s say a client approaches a lawyer with an employment discrimination case. The client believes they were wrongfully terminated based on their age. The lawyer turns to predictive analytics to analyze thousands of similar employment discrimination cases. The results consider factors such as the nature of the claim, the jurisdiction, the judge’s history, and the outcomes of past cases and conclude there is a 60% chance of winning if the case goes to trial. It also estimates potential damages, suggesting that if the client wins, they could receive between $100,000 and $300,000. The tool assesses past settlement trends in similar cases and predicts a 70% chance of reaching a settlement. It also estimates that the settlement amount would likely be between $80,000 and $150,000. This highlights the risks of proceeding to trial, such as the possibility of losing the case and incurring significant legal fees. The lawyer also considers the time and emotional toll on the client. Based on all this, the lawyer advises the client on the potential outcomes. The client understands that while there is a reasonable chance of winning at trial, the settlement offers a more certain and quicker resolution with less risk. The client decides to pursue a settlement.

Finding the Right Tool for You

When you are ready to get on board with predictive analytics, here are some considerations for choosing the right platform for your practice:

  1. Assess the Platform’s Data Sources. Ensure the platform draws from comprehensive, up-to-date legal databases. It should include case law, statutes, court rulings, and data on judges and opposing counsel. Verify that the platform covers the jurisdictions relevant to your practice, as legal rules and case outcomes can vary significantly between states, regions, or countries. In the early days, most platforms focused on Federal court, only recently expanding into state and local courts. Make sure the platform has access to long-term historical data for more accurate predictions. One way to test this is through a thorough demo of the application.
  2. Evaluate the Accuracy of Predictions. Look for platforms that demonstrate a consistent track record of accurate predictions in real-world cases. Check for case studies, reviews, or testimonials from legal professionals who have used the tool. You should look for a tool that provides reasoned predictions on aspects such as the likelihood of winning, judicial behavior, settlement probabilities, and expected timeframes.
  3. Consider Whether the Platform Is Customizable, Flexible, and User-Friendly. You should look for a platform that allows customization based on the specific facts of your case, jurisdiction, or legal area. The ability to adjust variables and inputs will provide more tailored insights. The platform should have a user-friendly interface that allows for easy navigation, data entry, and analysis without requiring extensive technical expertise.
  4. Data Privacy and Security. Ensure the platform adheres to strict data privacy and security standards, as it will likely involve sensitive client information. Check for compliance with legal data privacy laws, such as GDPR or CCPA.

As predictive analytics continues to evolve, it will become an increasingly important tool in legal practice, driving data-driven decision-making and enhancing strategic outcomes. As legal professionals embrace predictive analytics, its integration into daily practice will not only improve efficiency and reduce costs but also transform the way lawyers approach litigation and client service.

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