Law and AI

The integration of AI into law is moving from administrative support to core analytic and predictive functions, redefining expertise. In litigation, tools like Lex Machina or Ravel Law (now owned by LexisNexis) analyze millions of court documents. 

A lawyer preparing a motion for summary judgment in a patent case in a specific federal district can use these platforms to generate a data-driven profile: Judge X grants summary judgment on patent invalidity 40% of the time, but only 15% of the time when the moving party is the defendant; the average time from filing to decision is 8 months; the opposing firm most frequently cites KSR v. Teleflex in their successful oppositions. This transforms strategy from intuition-based guesswork to informed calculation. 

In corporate practice, AI for contract review, such as that from companies like Kira Systems or Luminance, operates at a scale impossible for humans. During a large merger, the AI can review thousands of supplier contracts across the target company in hours, flagging every "change-of-control" clause, non-standard indemnity provision, or unusual arbitration agreement. It does not just find keywords; it understands context, distinguishing a routine termination clause from one triggered by merger activity.

The effects?

This efficiency comes with profound professional and ethical shifts. The classic training of young associates-hours of document review-is diminishing. The value of a lawyer is ascending from information retrieval to complex judgment, negotiation, and client counseling based on the AI's output. However, the risks are significant, particularly in criminal justice. Predictive policing algorithms, such as PredPol or HunchLab, use historical crime data (arrests, reported incidents) to generate "heat maps" suggesting where patrols should be concentrated. 

The core flaw is that the training data reflects historical policing patterns, not actual crime rates. If a neighborhood was over-policed for decades, it will show high arrest data, leading the algorithm to recommend more policing, creating a self-fulfilling, discriminatory feedback loop. The algorithm systematizes past bias under a guise of neutrality.

Similarly, risk assessment tools like COMPAS or PSA, used in bail and sentencing, claim to predict a defendant's likelihood of reoffending. Studies, including the landmark investigation by ProPublica, found these tools were racially biased, falsely flagging Black defendants as future criminals at nearly twice the rate of white defendants. The danger is "automation bias": judges, presented with a numeric risk score from an opaque algorithm, may defer to it as objective science, even when it correlates with race or socioeconomic factors. The legal profession's new imperative is developing algorithmic accountability. 

Lawyers and judges must ask: What is the training data? What variables are used? How is the model validated? Can its reasoning be explained? 

The future requires lawyers who are not just users of technology, but its critical interrogators, ensuring these powerful tools are subjected to the same standards of fairness, due process, and transparency as any other evidence or procedure in a court of law.