The Role of Artificial Intelligence in Redefining Consent under Global Data Protection Frameworks

Background: Rapid advances in artificial intelligence (AI) — particularly systems that automate profiling, infer sensitive attributes, personalize interactions, and nudge behavior — are straining traditional concepts of consent embedded within global data protection frameworks. Established consent paradigms (informed, specific, freely given, and unambiguous) were designed for relatively discrete human–data interactions and are poorly matched to continuous, opaque, and ecosystemic AI data flows. Objective: This study examines how AI-driven processes are redefining the meaning, feasibility, and legal sufficiency of consent under major data protection regimes. It identifies normative and practical gaps between existing statutory consent requirements and the realities of AI-mediated data processing, and it proposes adaptive regulatory and technical responses. Methods: Employing an interdisciplinary methodology, the research combines doctrinal analysis of leading data protection instruments (e.g., GDPR-style regimes, US state laws, and select global counterparts), comparative case studies of enforcement outcomes, semi-structured interviews with regulators and privacy professionals, and scenario-based technical analysis of AI applications (targeted advertising, predictive analytics, and autonomous decision-making). The study synthesizes legal, ethical, and socio-technical perspectives to evaluate both legal interpretation and operational compliance. Results: The analysis finds that AI undermines core assumptions of conventional consent: opacity and model-driven inference impair meaningful information disclosure; personalization and continuous profiling make “specific” and time-limited consent impracticable; and asymmetries in technical literacy and market power create systemic coercion risks. Jurisdictions that treat consent as the primary lawful basis face heightened enforcement and legitimacy challenges when AI is central to processing. Conversely, frameworks emphasizing accountability, purpose limitation, and data-protection-by-design show greater resilience. Conclusions and Recommendations: To preserve autonomy and protect fundamental rights in the AI era, the paper recommends a multi-pronged approach: (1) reconceptualize consent as dynamic, context-sensitive, and supplementary rather than universally foundational; (2) mandate enhanced transparency measures and machine-readable notices coupled with meaningful explainability obligations; (3) require algorithmic impact assessments, stronger accountability mechanisms, and selective prohibitions on high-risk profiling; and (4) promote interoperable technical standards for consent management and revocation. These reforms aim to align legal doctrine with technological realities and to enable enforceable, rights-respecting governance of AI-driven data ecosystems.