Reputation Management in the ChatGPT Era

2024-12-09

arXiv.org

AI Summary

Generative AI systems (e.g., ChatGPT) frequently produce false, sensitive, or misleading content about real individuals—even without explicit prompting—posing severe reputational and privacy risks. Method: This study employs interdisciplinary legal-technical analysis, comparative law research, and compliance feasibility assessment to systematically examine the applicability and structural limitations of defamation law and data protection law in addressing such harms—particularly regarding preventive capacity, enforceability, and cross-jurisdictional coordination. Contribution/Results: The paper innovatively proposes the data subject’s rights to erasure (“right to be forgotten”) and rectification as core remedial mechanisms. It further advocates a paradigm shift from individual redress toward systemic “information ecosystem” governance. By integrating doctrinal rigor with regulatory pragmatism, the study advances a novel, theoretically grounded, and policy-viable framework for personality rights protection in the AI era.

Technology Category

• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Humans and AI: AI for Accessibility
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Mitigating online harms, including harassment, spam, and fake reviews

Abstract

Generative AI systems often generate outputs about real people, even when not explicitly prompted to do so. This can lead to significant reputational and privacy harms, especially when sensitive, misleading, and outright false. This paper considers what legal tools currently exist to protect such individuals, with a particular focus on defamation and data protection law. We explore the potential of libel law, arguing that it is a potential but not an ideal remedy, due to lack of harmonization, and the focus on damages rather than systematic prevention of future libel. We then turn to data protection law, arguing that the data subject rights to erasure and rectification may offer some more meaningful protection, although the technical feasibility of compliance is a matter of ongoing research. We conclude by noting the limitations of these individualistic remedies and hint at the need for a more systemic, environmental approach to protecting the infosphere against generative AI.

Problem

• Legal protection against AI-generated reputational harms
• Defamation law limitations in AI context
• Data protection law's role in AI outputs

Innovation

• Explores libel law limitations
• Assesses data protection remedies
• Advocates systemic infosphere protection