Lawyering in the Age of Artificial Intelligence

Paper · Source
Domain Specialization in LLMs

Source: Choi, Monahan, Schwarcz, Minnesota Law Review · 2024-11

We conducted the first randomized controlled trial to study the effect of AI assistance on human legal analysis. We randomly assigned law school students to complete realistic legal tasks either with or without the assistance of GPT-4, tracking how long the students took on each task and blind-grading the results.

We found that access to GPT-4 only slightly and inconsistently improved the quality of participants’ legal analysis but induced large and consistent increases in speed. AI assistance improved the quality of output unevenly—where it was useful at all, the lowest-skilled participants saw the largest improvements. On the other hand, AI assistance saved participants roughly the same amount of time regardless of their baseline speed. In follow-up surveys, participants reported increased satisfaction from using AI to complete legal tasks and correctly guessed the tasks for which GPT-4 was most helpful.

These results have important descriptive and normative implications for the future of lawyering. Descriptively, they suggest that AI assistance can significantly improve productivity and satisfaction, and that it can be selectively employed by lawyers in areas where AI is most useful. Because AI tools have an equalizing effect on performance, they may also promote equality in a famously unequal profession. Normatively, our findings suggest that law schools, lawyers, judges, and clients should thoughtfully embrace AI tools and plan for a future in which they will become widespread.

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Research framings built by reading the notes related to this paper — the questions it feeds into.

Why do perceived AI productivity gains often fail to translate into measured business impact? How do chatbot properties shape user belief adoption and resistance? Does generative AI shift knowledge work away from collaborative tasks? How should evaluation expand to capture deployment realities? How should control and autonomy be allocated in human-AI workplace collaboration? How should retrieval systems adapt to different reasoning problems? How reliably can detection systems identify AI-generated hallucinations and false claims? How do attention patterns affect model performance across varying context lengths? How do standardized benchmarks differ from measures of real-world AI capability? How do user perceptions and UI design drive consciousness attribution to AI? Why do users misjudge the credibility of AI-generated and verified content? How do users build accurate mental models when agent reasoning is partially hidden? How do confidence signals and expertise language mislead users' trust calibration? Should AI systems make failures transparent so users can contest and adjust? How does AI assistance affect human cognitive skill development over time?