A Legal Risk Taxonomy for Generative Artificial Intelligence

TL;DR

The paper addresses the absence of a structured legal risk framework for generative AI by analyzing 29 lawsuits to identify seven core claims and cataloging 30 speculative claims, with distinctions for pre- and post-deployment contexts. It details the elements and potential remedies for each claim, and proposes mitigation strategies (pre- and post-deployment) along with open questions to guide future research and policy. The taxonomy aims to help developers, policymakers, and researchers anticipate litigation risk, implement protective practices, and shape safer deployment of GenAI. The work emphasizes the novelty of GenAI and the need for thoughtful regulatory and technical safeguards, including possible safe harbors and the adaptation of traditional tort and contract principles to probabilistic AI systems.

Abstract

For the first time, this paper presents a taxonomy of legal risks associated with generative AI (GenAI) by breaking down complex legal concepts to provide a common understanding of potential legal challenges for developing and deploying GenAI models. The methodology is based on (1) examining the legal claims that have been filed in existing lawsuits and (2) evaluating the reasonably foreseeable legal claims that may be filed in future lawsuits. First, we identified 29 lawsuits against prominent GenAI entities and tallied the claims of each lawsuit. From there, we identified seven claims that are cited at least four times across these lawsuits as the most likely claims for future GenAI lawsuits. For each of these seven claims, we describe the elements of the claim (what the plaintiff must prove to prevail) and provide an example of how it may apply to GenAI. Next, we identified 30 other potential claims that we consider to be more speculative, because they have been included in fewer than four lawsuits or have yet to be filed. We further separated those 30 claims into 19 that are most likely to be made in relation to pre-deployment of GenAI models and 11 that are more likely to be made in connection with post-deployment of GenAI models since the legal risks will vary between entities that create versus deploy them. For each of these claims, we describe the elements of the claim and the potential remedies that plaintiffs may seek to help entities determine their legal risks in developing or deploying GenAI. Lastly, we close the paper by noting the novelty of GenAI technology and propose some applications for the paper's taxonomy in driving further research.