Lawyering in the Age of Artificial Intelligence
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.
Lines of inquiry this paper opens 24
Research framings built by reading the notes related to this paper — the questions it feeds into.
What are the real-world consequences of AI citation hallucinations?- Does speed improvement from AI assistants carry over to billable legal work?
- Can GenAI help with legal work if designed with audit trails?
- How do different legal AI tools compare in accuracy across case eras?
- What happens when lawyers rely on AI citations that turn out false?
- What percentage of AI hallucination cases result in actual court sanctions?
- Do solo lawyers face different citation hallucination risks than large firms?
- How much do existing legal AI tools actually hallucinate in practice?
- Why do lawyers need provenance tracking more than other professions?
- What upstream work takes lawyers most time in fact verification?
- Does retrieval augmented generation actually eliminate hallucinations in any domain?
- Can architectural changes reduce hallucination without external retrieval or verification?
- Why do hallucination rates differ between vendor AI products and student-used models?
- Do legal AI tools marketed as hallucination-free actually hallucinate?
- Do certain news topics trigger more hallucinations than others?
- How do courts distinguish between AI hallucinations and ordinary typographical errors?
- How often do AI book summaries fabricate details when spot-checks are random?
- Do companies time productivity claims to coincide with public offerings or fundraising?
- How much labor does AI verification actually save compared to full manual review?