A Legal Risk Taxonomy for Generative Artificial Intelligence
David Atkinson, Jacob Morrison

TL;DR
This paper develops a comprehensive taxonomy of legal risks associated with generative AI by analyzing existing lawsuits and predicting future legal claims, aiding developers and deployers in understanding potential legal challenges.
Contribution
It introduces the first detailed legal risk taxonomy for GenAI, based on analysis of lawsuits and foreseeable claims, distinguishing risks before and after deployment.
Findings
Identified 7 common legal claims in existing GenAI lawsuits.
Mapped 30 potential legal claims, with 19 pre-deployment and 11 post-deployment.
Provided detailed claim elements and potential remedies for each identified risk.
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…
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Taxonomy
TopicsEthics and Social Impacts of AI · Law, AI, and Intellectual Property
