Kevin Tobia (Georgetown University Law Center; Georgetown University – Department of Philosophy) has posted Algorithmic Legal Interpretation (University of Chicago Law Review Online (forthcoming 2024)) on SSRN. Here is the abstract:
Legal interpretation has taken an empirical turn, with scholars and judges debating the use of corpus linguistics, surveys, and experiments in interpretation. Professor Choi’s Measuring Clarity in Legal Text offers a new proposal: interpretation by artificial intelligence. The Article impressively and thoughtfully considers contributions from word embeddings, representations of naturally occurring language in a multi-dimensional vector space, driven by machine learning algorithms.
The Article expresses some caution and some optimism about its proposal. This Response endorses the caution: Words’ proximity in vector space (measured by cosine similarity) is not conclusive of a legal text’s clarity or ambiguity, and judges should not rely on such outputs of algorithmic tools to settle interpretation. Nor should judges look to the outputs of ChatGPT or other LLMs as answers to legal interpretation. Nevertheless, the Article’s new empirical approach usefully illuminates central assumptions and tensions in legal interpretive theories. In sum, Measuring Clarity in Legal Text is an important contribution, opening new, timely, and rich debates about artificial intelligence’s contributions to legal interpretation.
Recommended.