Book contents
5 - Can AI Be Consentful?
Published online by Cambridge University Press: 24 July 2026
Summary
This chapter examines how traditional consent frameworks fail to address generative AI systems creating content from personal data. While individuals can consent to initial data use for AI training, they cannot meaningfully consent to the vast array of potential outputs or their subsequent use and distribution. We identify three core challenges creating a "consent gap": the scope problem (inability to predict all possible outputs), the temporality problem (consent cannot cover future unknown uses), and the autonomy trap (consenting to uses that may undermine personal autonomy). These issues are problematic when AI-generated content creates new forms of personal representation beyond original consent boundaries. Current legal frameworks inadequately protect individual autonomy, identity rights, and social responsibility in AI contexts. By connecting these consent limitations to broader responsible AI principles (fairness, transparency, accountability, and autonomy), we argue for evolving ethical and legal approaches that better address generative AI’s challenges to traditional consent models.
Information
- Type
- Chapter
- Information
- Operationalizing Responsible AI , pp. 86 - 109Publisher: Cambridge University PressPrint publication year: 2026
References
Apple Inc. 2017. Differential privacy overview. Apple Inc. www.apple.com/privacy/docs/Differential_Privacy_Overview.pdf (last accessed February 19, 2026).Google Scholar
Bergemann, B. 2018. The consent paradox: Accounting for the prominent role of consent in data protection. In Hansen, M., Kosta, E., Nai-Fovino, I., and Fischer-Hübner, S. (eds.), Privacy and Identity Management. The Smart Revolution (Cham: Springer), pp. 111–131.CrossRefGoogle Scholar
Berger, V. 2024 (August 21). SAG-AFTRA’s AI deal: A $5 billion gamble on the future of voice acting. Forbes. www.forbes.com/sites/virginieberger/2024/08/21/sag-aftras-ai-deal-a-5-billion-gamble-on-the-future-of-voice-acting/ (last accessed February 19, 2026).Google Scholar
Bogucka, E., Constantinides, M., Šćepanović, S., and Quercia, D. 2024. AI design: A responsible AI framework for impact assessment reports. IEEE Internet Computing. http://doi.org/10.1109/MIC.2024.3451351.CrossRefGoogle Scholar
Bourtoule, L., Chandrasekaran, V., Choquette-Choo, C. A., Jia, H., Travers, A., Zhang, B., et al. 2021. Machine unlearning. 2021 IEEE Symposium on Security and Privacy (SP). (New York: IEEE)Google Scholar
Code of Nuremberg. 1949. The Nuremberg code. Trials of War Criminals before the Nuremberg Military Tribunals under Control Council Law 10.1949 (1949): 181–2.Google Scholar
Custers, B. H. M. 2016. Click here to consent forever: Expiry dates for informed consent. Big Data & Society, 3. https://doi.org/10.1177/2053951715624935.CrossRefGoogle Scholar
Datavant. 2018 (Oct.). Matching accuracy of patient tokens in de-identified health data sets: A false positive analysis. Datavant. www.trillianthealth.com/hubfs/Datavant_Matching-Accuracy-Whitepaper.pdf (last accessed February 20, 2026).Google Scholar
Desai, D. R., and Riedl, M. 2024. Between copyright and computer science: The law and ethics of generative AI. Northwestern Journal of Technology and Intellectual Property, 22(1), 55–108.Google Scholar
Duhigg, C. 2012 (Feb. 19). How companies learn your secrets. New York Times, www.nytimes.com/2012/02/19/magazine/shopping-habits.html (last accessed February 19, 2026).Google Scholar
Edelstein, L., trans. 1943. The Hippocratic Oath: Classical version. In The Hippocratic Oath: Text, Translation, and Interpretation (Baltimore: Johns Hopkins Press), translation from the Greek. www.pbs.org/wgbh/nova/doctors/oath_classical.html (last accessed February 19, 2026).Google Scholar
Flick, C. 2009. Informed consent in information technology: Improving user experience. PhD dissertation., Charles Sturt University, Centre for Applied Philosophy and Public Ethics.Google Scholar
Grynbaum, M. M., Sperling, N., Koblin, J., and Barnes, B. 2023 (July 13). Hollywood actors go on strike, shutting down film and TV productions. New York Times. www.nytimes.com/live/2023/07/13/business/actors-strike-sag (last accessed February 19, 2026).Google Scholar
Hasday, L. R. 2001. The Hippocratic oath as literary text: A dialogue between law and medicine. Yale Journal of Health Policy, Law, and Ethics, 2, 299.Google Scholar
Hern, A. 2024 (May 20). Scarlett Johansson’s OpenAI clash is just the start of legal wrangles over artificial intelligence. The Guardian. www.theguardian.com/technology/article/2024/may/20/chatgpt-scarlett-johansson-voice (last accessed February 19, 2026).Google Scholar
Hill, K. 2012 (Feb. 16). How Target figured out a teen girl was pregnant before her father did. Forbes. www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/ (last accessed February 19, 2026).Google Scholar
Hill, K. 2020 (Jan. 18). The secretive company that might end privacy as we know it. New York Times. www.nytimes.com/2020/01/18/technology/clearview-privacy-facial-recognition.html (last accessed February 19, 2026).Google Scholar
IBM. 2024. Using AI factsheets for AI governance. IBM. https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/factsheets-model-inventory.html?context=cpdaas (last accessed February 19, 2026).Google Scholar
Johnson, D. G. 2006. Computer systems: Moral entities but not moral agents. Ethics and Information Technology, 8, 195–204.CrossRefGoogle Scholar
Longpre, S., Mahari, R., Lee, A., Lund, C., Oderinwale, H., Brannon, W., et al. 2024. Consent in crisis: The rapid decline of the AI data commons. In Proceedings of the 38th Conference on Neural Information Processing Systems (NeurIPS 2024) Track on Datasets and Benchmarks. https://doi.org/10.48550/arXiv.2407.14933.CrossRefGoogle Scholar
Madianou, M. 2025. Technocolonialism: When Technology for Good is Harmful (Cambridge: Polity Press).Google Scholar
Manson, N. C., and O’Neill, O. 2007. Rethinking Informed Consent in Bioethics (Cambridge: Cambridge University Press).CrossRefGoogle Scholar
Miller, F., and Wertheimer, A. (eds.). 2009. The Ethics of Consent: Theory and Practice (Oxford: Oxford University Press).CrossRefGoogle Scholar
Muller, M. 2024. Data silences: How to unsilence the uncertainties in data science. In Proceedings of the 2024 SIAM International Conference on Data Mining (SDM) (Philadelphia, PA: Society for Industrial and Applied Mathematics), pp. 388–391. https://doi.org/10.1137/1.9781611978032.44.CrossRefGoogle Scholar
Muller, M., and Strohmayer, A. 2022 (April). Forgetting practices in the data sciences. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, pp. 1–19. https://doi.org/10.1145/3491102.3517644.CrossRefGoogle Scholar
Partnership on AI. 2021 (Aug.). Annotation and benchmarking on understanding and transparency of machine learning lifecycles (ABOUT ML), Version 0 Draft. Partnership on AI. https://partnershiponai.org/wp-content/uploads/2021/08/ABOUT-ML-v0-Draft-Final.pdf (last accessed February 19, 2026).Google Scholar
Phemex Academy. 2021 (Sept. 24). What is ocean protocol: Blockchain data marketplace. Phemex Academy. https://phemex.com/academy/what-is-ocean-protocol (last accessed February 19, 2026).Google Scholar
Rawls, J. 1971. A Theory of Justice (Cambridge, MA: Harvard University Press).CrossRefGoogle Scholar
SAG-AFTRA. 2024a (Aug. 20). SAG-AFTRA and Narrativ announce new agreement. SAG-AFTRA. www.sagaftra.org/sag-aftra-and-narrativ-announce-new-agreement (last accessed February 19, 2026).Google Scholar
SAG-AFTRA. 2024b (Jan. 9). SAG-AFTRA and Replica Studios introduce groundbreaking AI voice agreement at CES. SAG-AFTRA. www.sagaftra.org/sag-aftra-and-replica-studios-introduce-groundbreaking-ai-voice-agreement-ces (last accessed February 19, 2026).Google Scholar
Solove, D. J. 2013. Privacy self-management and the consent dilemma. Harvard Law Review, 126, 1880.Google Scholar
Stahl, B. C., Antoniou, J., Bhalla, N., Brooks, L., Jansen, P., Lindqvist, B., et al. 2023. A systematic review of artificial intelligence impact assessments. Artificial Intelligence Review, 56(11), 12799–12831. https://doi.org/10.1007/s10462-023-10420-8.CrossRefGoogle Scholar
Treasury Board of Canada Secretariat. 2024. Algorithmic impact assessment. Treasury Board of Canada Secretariat. https://open.canada.ca/data/en/dataset/5423054a-093c-4239-85be-fa0b36ae0b2e (last accessed February 19, 2026).Google Scholar
Tschider, C. A. 2021. Meaningful choice: A history of consent and alternatives to the consent myth. North Carolina Journal of Law & Technology, 22(4), 617.Google Scholar
United Nations General Assembly. 1948. The Universal Declaration of Human Rights (UDHR) (New York: United Nations General Assembly).Google Scholar
Warren, S. D., and Brandeis, L. D. 1890 (Dec. 15). The right to privacy. Harvard Law Review, 4(5), 193–220. https://doi.org/10.2307/1321160.CrossRefGoogle Scholar
Xia, B., Lu, Q., Perera, H., Zhu, L., Xing, Z., Liu, Y., et al. 2023 (May). Towards concrete and connected AI risk assessment (C 2 AIRA): A systematic mapping study. In 2023 IEEE/ACM 2nd International Conference on AI Engineering–Software Engineering for AI (CAIN) (New York: IEEE), pp. 104–116. http://doi.org/10.1109/CAIN58948.2023.00027.Google Scholar
Accessibility standard: WCAG 2.2 AAA
Why this information is here
This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.Accessibility Information
The HTML of this chapter complies with version 2.2 of the
Web Content Accessibility Guidelines
(WCAG), offering more comprehensive accessibility measures for a broad range
of users and attains the highest
(AAA)
level of WCAG compliance, optimising the user experience by meeting the most extensive
accessibility guidelines.
Content Navigation
Table of contents navigation
Allows you to navigate directly to chapters, sections, or non‐text items through a linked table of contents, reducing the need for extensive scrolling.
Allows you to navigate directly to chapters, sections, or non‐text items through a linked table of contents, reducing the need for extensive scrolling.
Index navigation
Provides an interactive index, letting you go straight to where a term or subject appears in the text without manual searching.
Provides an interactive index, letting you go straight to where a term or subject appears in the text without manual searching.
Reading Order & Textual Equivalents
Single logical reading order
You will encounter all content (including footnotes, captions, etc.) in a clear, sequential flow, making it easier to follow with assistive tools like screen readers.
You will encounter all content (including footnotes, captions, etc.) in a clear, sequential flow, making it easier to follow with assistive tools like screen readers.
Short alternative textual descriptions
You get concise descriptions (for images, charts, or media clips), ensuring you do not miss crucial information when visual or audio elements are not accessible.
You get concise descriptions (for images, charts, or media clips), ensuring you do not miss crucial information when visual or audio elements are not accessible.
Full alternative textual descriptions
You get more than just short alt text: you have comprehensive text equivalents, transcripts, captions, or audio descriptions for substantial non‐text content, which is especially helpful for complex visuals or multimedia.
You get more than just short alt text: you have comprehensive text equivalents, transcripts, captions, or audio descriptions for substantial non‐text content, which is especially helpful for complex visuals or multimedia.
Visualised data also available as non-graphical data
You can access graphs or charts in a text or tabular format, so you are not excluded if you cannot process visual displays.
You can access graphs or charts in a text or tabular format, so you are not excluded if you cannot process visual displays.
Visual Accessibility
Use of high contrast between text and background colour
You benefit from high‐contrast text, which improves legibility if you have low vision or if you are reading in less‐than‐ideal lighting conditions.
You benefit from high‐contrast text, which improves legibility if you have low vision or if you are reading in less‐than‐ideal lighting conditions.