Research

This paper studies how generative AI has reshaped entry into the federal civil court system. Drawing on administrative records covering more than 4.5 million non-prisoner federal civil court cases from FY2005-FY2026 and 46 million PACER docket entries matched to those cases, we document three sets of findings. First, the number of pro se cases—or self-represented cases—is increasing dramatically, rising from a long-term steady-state average of 11% to 16.8% in FY2025. This increase is concentrated in case types characterized by formulaic document production and absent from more complex, attorney-intensive categories. Second, we argue these cases are placing larger burden on federal district courts. Pro se cases are not terminating faster, and this combined with the increased case numbers suggests more cases for judges to process. Moreover, intra-case activity is up, with the total volume of docket entries per court generated by pro se cases in their first 180 days up 158% from pre-AI means to 2025. Third, we directly validate that AI use is increasing in federal courts. Using a random sample of 1,600 complaints drawn from an 8-year period (2019-2026), we find that a large and growing share of complaints are flagging positive for AI-generated text, from essentially zero in the pre-AI period to more than 18% in 2026.

We conduct a survey of economists and a representative sample of Americans to infer the reduction in the perceived value of a paper when its authors have conflicts of interest (CoI), i.e., they have financial, professional, or ideological stakes in the outcome of the results. On average, a CoI decreases trust in the conclusions of an economics paper by 30%. This reduction in trust reflects a combination of the frequency of conflicted papers and the bias of papers when they are conflicted. To isolate the second term, we introduce a key construct: the CoI Discount, which measures the reduction in the value of a conflicted paper relative to a non-conflicted one. We show that, on average, conflicted papers are worth less than half of non-conflicted ones, though this effect varies significantly depending on the nature of the conflict. The discount is more pronounced when the conflict involves the interest of a private rather than a public entity. Restricted data access also leads to a substantial discount. We validate our survey based estimates by comparing them to actual biases observed in conflicted papers within the economics and medical literature.