Legal theorists seek to persuade other jurists of certain theories: Textualism or purposivism; formalism or realism; natural law theory or positivism; prison reform or abolition; universal or particular human rights? Despite voluminous literature about these debates, tremendous uncertainty remains about which views experts endorse. This Article presents the first-ever empirical study of American law professors about legal theory questions. A novel dataset of over six hundred law professors reveals expert consensus and dissensus about dozens of longstanding legal theory debates. -/- (...) Law professors also debate questions about the nature of the legal academy. Descriptively, which subjects (e.g. constitutional law) and methods (e.g. law & economics) are most central within the legal academy today? And prescriptively, should today’s legal academy prioritize additional areas (e.g. legislation) or methods (e.g. critical race theory)? There is great interest in these questions but no empirical dataset of experts’ views; this results in uncertainty about which views experts endorse. This Article’s empirical study also clarifies these questions, documenting law professors’ evaluation of over one-hundred areas of law. -/- The legal theory and legal academy findings support implications for legal scholarship, education, and practice. Clearly, debates about law and the legal academy’s evolution should not be settled by a survey. Nevertheless, insofar as law professors are experts about these issues, it is instructive to discover and carefully examine what views those experts hold, so as to help determine which views are most likely to be true and how the legal academy ought to develop. (shrink)

Perhaps the most widely touted of GPT-4’s at-launch, zero-shot capabilities has been its reported 90th-percentile performance on the Uniform Bar Exam. This paper begins by investigating the methodological challenges in documenting and verifying the 90th-percentile claim, presenting four sets of findings that indicate that OpenAI’s estimates of GPT-4’s UBE percentile are overinflated. First, although GPT-4’s UBE score nears the 90th percentile when examining approximate conversions from February administrations of the Illinois Bar Exam, these estimates are heavily skewed towards repeat test-takers (...) who failed the July administration and score significantly lower than the general test-taking population. Second, data from a recent July administration of the same exam suggests GPT-4’s overall UBE percentile was below the 69th percentile, and $$\sim$$ ∼ 48th percentile on essays. Third, examining official NCBE data and using several conservative statistical assumptions, GPT-4’s performance against first-time test takers is estimated to be $$\sim$$ ∼ 62nd percentile, including $$\sim$$ ∼ 42nd percentile on essays. Fourth, when examining only those who passed the exam (i.e. licensed or license-pending attorneys), GPT-4’s performance is estimated to drop to $$\sim$$ ∼ 48th percentile overall, and $$\sim$$ ∼ 15th percentile on essays. In addition to investigating the validity of the percentile claim, the paper also investigates the validity of GPT-4’s reported scaled UBE score of 298. The paper successfully replicates the MBE score, but highlights several methodological issues in the grading of the MPT + MEE components of the exam, which call into question the validity of the reported essay score. Finally, the paper investigates the effect of different hyperparameter combinations on GPT-4’s MBE performance, finding no significant effect of adjusting temperature settings, and a significant effect of few-shot chain-of-thought prompting over basic zero-shot prompting. Taken together, these findings carry timely insights for the desirability and feasibility of outsourcing legally relevant tasks to AI models, as well as for the importance for AI developers to implement rigorous and transparent capabilities evaluations to help secure safe and trustworthy AI. (shrink)