UC Berkeley Public Law Research Paper Forthcoming: Generative AI and Legal Aid: Results from a Field Study and 100 Use Cases to Bridge the Access to Justice Gap

Abstract

How can AI tools be used to address the access to justice gap — the millions of low-income Americans that lack adequate legal assistance for 90% of their civil legal problems? We conducted the first field study of lawyers using generative AI of which we are aware and a companion survey of 202 legal aid professionals to find out. A cohort of 91 people received 1-2 months of access to paid generative artificial intelligence tools, a randomly selected subset of which also received “concierge” support including peer use cases, office hours, and assistance. Following the pilot, 90% of pilot participants reported increased productivity and 75% reported their intent to continue using generative AI tools. While concerns remained, pilot participants were able to manage risks by focusing on lower-risk applications like document summarization, confirmatory or preliminary research, the production of first drafts, and translation, from legalese or English into more accessible formats. Before the trial, women were far less likely than men to use or value the tools. By the trial’s end, men’s and women’s outcomes across various measures were statistically indistinguishable. Participants receiving concierge services had significantly better outcomes than the control group across a range of metrics.

These results suggest that generative AI tools can significantly enhance legal professionals and narrow the justice gap, but that how they are introduced matter – though women comprise the majority of public interest lawyers, organic uptake of generative AI was much higher among men in our study. Assistance can also improve tool adoption. The participants’ positive experiences support viewing AI technologies as augmenting rather than threatening the work of lawyers. As we document, legal-aid lawyer directed technological solutions may have the greatest potential to not just marginally, but dramatically, increase service coverage, and we suggest some steps, such as exploring regulatory sandboxes and devising ways to institute voluntary certification or “seal of approval” programs verifying the quality of legal aid bots to support such generative collaborations. Along with the paper, we release a companion database of 100 helpful use cases, including prompts and outputs, provided by legal aid professionals in the trial, to support broader adoption of AI tools.