This article examines how national legal structures affect the reception of the European Union framework for digital preservation and public-domain access outside the Union. Articles 6, 7 and 14 of Directive 2019/790 provide the benchmark. They address preservation copying by cultural heritage institutions, protection of exceptions against contractual restrictions and their connection with rules on technological protection measures, and the exclusion of new copyright or related rights in non-original reproductions of public-domain visual art. The analysis identifies two regulatory configurations among EU Member States: copyright-centered implementation of the Directive, and copyright implementation operating alongside non-harmonized national public-law controls over cultural heritage reproductions. Ukraine and Türkiye are examined as two ways of responding to EU copyright rules. Ukraine has moved closer to the EU model through copyright reform and participation in European digital cultural heritage initiatives. Its legislation already covers some functions reflected in Articles 6 and 14, but gaps remain. These include contractual override, the use of works for preservation when technological protection measures prevent access, and the status of non-original reproductions of public-domain visual art. Türkiye follows a different approach. Copyright, legal deposit, and cultural heritage rules regulate these issues separately. Electronic legal deposit supports preservation through the National Library, but there is no copyright exception allowing cultural heritage institutions to preserve collections. Administrative permissions and controls also affect the creation and reuse of cultural heritage reproductions. The comparison identifies three modes of functional reception–direct legal transmission, institutional and procedural transmission, and functional convergence–and treats non-transmission as a separate category for EU functions left unperformed. In the cases examined, reception is more complete where copyright permissions, technological safeguards, legal-deposit mechanisms and cultural heritage administration are institutionally coordinated. Equivalent protection for preservation and public-domain access depends on the coordinated legal and institutional effects of the national framework.

Artificial intelligence (AI)-driven genomic research challenges prevailing consent frameworks by enabling iterative data reuse, cumulative inference, and intergenerational risk that static, one-off consent cannot adequately address. This article applies the Social Construction of Technology (SCOT) and the principle of Respect for Persons (PRP), interpreted through relational autonomy, to examine how regulatory categories such as broad consent, minimal risk, and compatible purpose are socially constructed, institutionally stabilised, and ethically consequential. In this context, consent rules operate not merely as mechanisms of authorisation but as governance tools that allocate responsibility, shape expectations, and determine the legitimacy of downstream data use over time. Through a comparative analysis of the EU General Data Protection Regulation (GDPR) and the U.S. Common Rule (U.S.C.R.), the article demonstrates how categorical exemptions and procedural consent under the U.S.C.R. fail to ensure sustained participant protection in genomic AI, while the GDPR embeds consent within a cumulative legality architecture grounded in Articles 5, 6(4), 9, and 89(1), with Recital 33 guiding interpretation. The article argues that dynamic consent does not constitute an independent lawful basis under the GDPR but may offer a doctrinally compatible and ethically robust governance mechanism capable of operationalising existing GDPR obligations. It may do so by enabling iterative engagement, granular permissions, and accountability across evolving research contexts, particularly in dual-regulation environments such as Qatar, where GDPR-style frameworks coexist with U.S.C.R. requirements.

The development of generative AI relies on large-scale corpora that often contain personal information, making anonymization central to data-protection compliance. In comparative law, anonymization generally determines whether personal-information protection rules apply, with the key inquiry turning on identifiability and the reasonable likelihood of re-identification. In generative AI, however, parameterized learning, dynamic content generation, and long-term interaction have transformed anonymization from a static processing outcome into a probabilistic and context-dependent risk condition. This creates three main challenges: a mismatch between formal anonymization and substantive risk, which may lead to identity disclosure or sensitive-attribute inference; the erosion of anonymization’s function as a regulatory boundary, which may enable regulatory circumvention and undermine public trust; and uncertainty over responsibility allocation, which may hinder risk prevention, enforcement, and remedies. Existing rules offer only limited responses to residual risks, jointly produced risks, and dynamic changes in model operation. Accordingly, anonymization should be reconceptualized as an ongoing governance framework based on context-sensitive identifiability standards, tiered legal consequences, responsibility allocation, and lifecycle oversight, thereby reconciling personal-information protection with AI development.