Towards a Harms Taxonomy of AI Likeness Generation
Ben Bariach, Bernie Hogan, Keegan McBride

TL;DR
This paper develops a comprehensive harms taxonomy related to AI-generated likenesses, addressing legal, ethical, and societal issues to guide responsible deployment and policy-making.
Contribution
It introduces a novel taxonomy of harms from AI likeness generation, grounded in legal history and philosophical analysis, to inform mitigation strategies and policy.
Findings
Seven categories of harms identified
Conceptual framework for understanding likeness creation
Guidelines for responsible AI likeness deployment
Abstract
Generative artificial intelligence models, when trained on a sufficient number of a person's images, can replicate their identifying features in a photorealistic manner. We refer to this process as 'likeness generation'. Likeness-featuring synthetic outputs often present a person's likeness without their control or consent, and may lead to harmful consequences. This paper explores philosophical and policy issues surrounding generated likeness. It begins by offering a conceptual framework for understanding likeness generation by examining the novel capabilities introduced by generative systems. The paper then establishes a definition of likeness by tracing its historical development in legal literature. Building on this foundation, we present a taxonomy of harms associated with generated likeness, derived from a comprehensive meta-analysis of relevant literature. This taxonomy…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Ethics and Social Impacts of AI
