DAIL – the Database of AI Litigation

This database presents information about ongoing and completed litigation involving artificial intelligence, including machine learning. It covers cases from complaint forward – as soon as we learn of them – whether or not they generate published decisions. It is intended to be broad in scope, covering everything from algorithms used in hiring and credit and criminal sentencing decisions to liability for accidents involving autonomous vehicles. It also includes some cases concerning statistical analysis and data protection that may not directly involve artificial intelligence, but that are of particular relevance to AI projects. It includes cases addressing whether AIs can be authors of works protected by copyright law, or inventors of inventions protected by patent law, but it does not include litigation concerning patents that may involve artificial intelligence or machine learning.

If you know of AI litigation that you don’t see documented in the database, or if you have other suggestions, please tell us, using this contact form. Research and writing: GW Law students Jenna Fattah, Xiaonan (Caroline) Qu, Beatriz Beserra, Andrew Ware, Allie Schiele, Junhao Chen, Sydney Huppert, Zoe Kim, and Molly Brown, and Prof. Robert Brauneis. Implementation of the database on the Caspio platform: GW students Sean Zhao, Aneri Girishbhai Patel, Ji Wang, and Sydney Huppert, and Prof. Robert Brauneis. This database is based on work supported in part by the Institute for Trustworthy AI in Law and Society (TRAILS), which is supported by the National Science Foundation under Award No. 2229885. Any opinions, findings, and conclusions or recommendations expressed in this database are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.