Can AI be Consentful?

2025-06-27

AI Summary

This paper identifies the “consent gap” precipitated by generative AI—a systemic failure of traditional informed consent frameworks across three dimensions: scope (inability to cover vast volumes of derivative content), temporality (static consent mechanisms ill-suited to dynamic, iterative generation), and autonomy (superficial consent masking algorithmic domination). Through an interdisciplinary legal-ethical analysis grounded in responsible AI principles, it critically examines structural deficiencies in current governance regimes concerning privacy protection, identity rights, and individual agency. The study proposes a novel consent paradigm characterized by dynamism, revocability, and contextual adaptability. It further advocates for an adaptive governance framework that jointly upholds individual rights—including data subjects’ control over AI-generated outputs—and social responsibilities—such as developers’ accountability for downstream impacts. This work furnishes both theoretical foundations and institutional pathways for reconfiguring ethical and legal norms in the generative AI era.

Technology Category

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

Application Category

• Responsible Web: Consent frameworks and practices on the web
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

The evolution of generative AI systems exposes the challenges of traditional legal and ethical frameworks built around consent. This chapter examines how the conventional notion of consent, while fundamental to data protection and privacy rights, proves insufficient in addressing the implications of AI-generated content derived from personal data. Through legal and ethical analysis, we show that while individuals can consent to the initial use of their data for AI training, they cannot meaningfully consent to the numerous potential outputs their data might enable or the extent to which the output is used or distributed. We identify three fundamental challenges: the scope problem, the temporality problem, and the autonomy trap, which collectively create what we term a ''consent gap'' in AI systems and their surrounding ecosystem. We argue that current legal frameworks inadequately address these emerging challenges, particularly regarding individual autonomy, identity rights, and social responsibility, especially in cases where AI-generated content creates new forms of personal representation beyond the scope of the original consent. By examining how these consent limitations intersect with broader principles of responsible AI (including fairness, transparency, accountability, and autonomy) we demonstrate the need to evolve ethical and legal approaches to consent.

Problem

• Challenges of traditional consent frameworks in AI systems
• Insufficiency of consent for AI-generated content implications
• Need for evolved ethical and legal consent approaches

Innovation

• Analyzing AI consent gaps legally
• Identifying scope and temporality problems
• Proposing ethical AI consent frameworks