Can AI be Consentful?
- URL: http://arxiv.org/abs/2507.01051v1
- Date: Fri, 27 Jun 2025 15:32:16 GMT
- Title: Can AI be Consentful?
- Authors: Giada Pistilli, Bruna Trevelin,
- Abstract summary: generative AI systems expose the challenges of traditional legal and ethical frameworks built around consent.<n>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.
- Score: 0.5278958184444331
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Countering Privacy Nihilism [2.6212127510234797]
AI may be presumed capable of inferring "everything from everything"<n>Discarding data categories as a normative anchoring in privacy and data protection is what we call privacy nihilism.<n>We propose moving away from privacy frameworks that focus solely on data type, neglecting all other factors.
arXiv Detail & Related papers (2025-07-24T09:52:18Z) - Ethical AI: Towards Defining a Collective Evaluation Framework [0.3413711585591077]
Artificial Intelligence (AI) is transforming sectors such as healthcare, finance, and autonomous systems.<n>Yet its rapid integration raises urgent ethical concerns related to data ownership, privacy, and systemic bias.<n>This article proposes a modular ethical assessment framework built on ontological blocks of meaning-discrete, interpretable units.
arXiv Detail & Related papers (2025-05-30T21:10:47Z) - Artificial Intelligence in Government: Why People Feel They Lose Control [44.99833362998488]
The use of Artificial Intelligence in public administration is expanding rapidly.<n>While AI promises greater efficiency and responsiveness, its integration into government functions raises concerns about fairness, transparency, and accountability.<n>This article applies principal-agent theory to AI adoption as a special case of delegation.
arXiv Detail & Related papers (2025-05-02T07:46:41Z) - Technology as uncharted territory: Contextual integrity and the notion of AI as new ethical ground [55.2480439325792]
I argue that efforts to promote responsible and ethical AI can inadvertently contribute to and seemingly legitimize this disregard for established contextual norms.<n>I question the current narrow prioritization in AI ethics of moral innovation over moral preservation.
arXiv Detail & Related papers (2024-12-06T15:36:13Z) - Towards Responsible AI in Banking: Addressing Bias for Fair
Decision-Making [69.44075077934914]
"Responsible AI" emphasizes the critical nature of addressing biases within the development of a corporate culture.
This thesis is structured around three fundamental pillars: understanding bias, mitigating bias, and accounting for bias.
In line with open-source principles, we have released Bias On Demand and FairView as accessible Python packages.
arXiv Detail & Related papers (2024-01-13T14:07:09Z) - Survey on AI Ethics: A Socio-technical Perspective [0.9374652839580183]
Ethical concerns associated with AI are multifaceted, including challenging issues of fairness, privacy and data protection, responsibility and accountability, safety and robustness, transparency and explainability, and environmental impact.
This work unifies the current and future ethical concerns of deploying AI into society.
arXiv Detail & Related papers (2023-11-28T21:00:56Z) - Is the U.S. Legal System Ready for AI's Challenges to Human Values? [16.510834081597377]
This study investigates how effectively U.S. laws confront the challenges posed by Generative AI to human values.
We identify notable gaps and uncertainties within the existing legal framework regarding the protection of fundamental values.
We advocate for legal frameworks that evolve to recognize new threats and provide proactive, auditable guidelines to industry stakeholders.
arXiv Detail & Related papers (2023-08-30T09:19:06Z) - VerifAI: Verified Generative AI [22.14231506649365]
Generative AI has made significant strides, yet concerns about its accuracy and reliability continue to grow.
We propose that verifying the outputs of generative AI from a data management perspective is an emerging issue for generative AI.
Our vision is to promote the development of verifiable generative AI and contribute to a more trustworthy and responsible use of AI.
arXiv Detail & Related papers (2023-07-06T06:11:51Z) - Fairness in Agreement With European Values: An Interdisciplinary
Perspective on AI Regulation [61.77881142275982]
This interdisciplinary position paper considers various concerns surrounding fairness and discrimination in AI, and discusses how AI regulations address them.
We first look at AI and fairness through the lenses of law, (AI) industry, sociotechnology, and (moral) philosophy, and present various perspectives.
We identify and propose the roles AI Regulation should take to make the endeavor of the AI Act a success in terms of AI fairness concerns.
arXiv Detail & Related papers (2022-06-08T12:32:08Z) - Trustworthy AI: From Principles to Practices [44.67324097900778]
Many current AI systems were found vulnerable to imperceptible attacks, biased against underrepresented groups, lacking in user privacy protection, etc.
In this review, we strive to provide AI practitioners a comprehensive guide towards building trustworthy AI systems.
To unify the current fragmented approaches towards trustworthy AI, we propose a systematic approach that considers the entire lifecycle of AI systems.
arXiv Detail & Related papers (2021-10-04T03:20:39Z) - Bias in Data-driven AI Systems -- An Introductory Survey [37.34717604783343]
This survey focuses on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful Machine Learning (ML) algorithms.
If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features like race, sex, etc.
arXiv Detail & Related papers (2020-01-14T09:39:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.