Shattering the Illusion of Formal Compliance: Digital Consent as an Explainable Socio-Technical Process
| Art der Publikation: | Artikel in einem Konferenzbericht |
| Zitat: | |
| Buchtitel: | In Proceedings of EXTRAAMAS 2026 |
| Jahr: | 2026 |
| Monat: | September |
| Abriss: | The increasing adoption of AI-driven systems in domains such as healthcare, personalized assistance, and behavioural monitoring challenges the foundational assumptions underlying informed consent. Existing digital consent mechanisms provide traceability and regulatory compliance but often fail to ensure meaningful user understanding, reducing consent to a procedural artifact rather than a genuine expression of autonomy. In this paper, we argue that informed consent in AI-mediated environments should be reconceptualized as an explainability and governance problem. We propose an expanded view of Explainable Artificial Intelligence (XAI) that extends beyond model interpretability to support user understanding of data practices, automated inference, and system functionalities throughout the consent lifecycle. Building on legal analysis, cognitive science, and prior work on dynamic and agent-based consent management, we introduce a conceptual framework for explainable and verifiable consent. The framework is operationalized through five interdependent design principles: (i) modularization of consent content, (ii) layered and adaptive explanations, (iii) interactive verification of understanding, (iv) granular functionality control, and (v) temporal validation of consent. We further discuss the role of explainability as an accountability mechanism and critically examine risks associated with persuasive explanations, automated comprehension assessment, accessibility barriers, and functional coercion. By reframing consent as a dynamic socio-technical process embedded within intelligent systems, this work contributes to ongoing research on explainable AI, multi-agent systems, and AI governance, and outlines a research agenda for trustworthy and autonomy-preserving AI services. |
| Schlagworte: | AI Governance, Algorithmic Accountability, Dynamic Consent, Explainable AI, Informed Consent |
| Autoren | |
| Hinzugefügt von: | [] |
| Gesamtbewertung: | 0 |
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