The Commission concludes that an EU-level registry could help copyright holders communicate reservations against the use of their works for text and data mining, including AI-model training, while enabling developers to identify those reservations more consistently. The proposed approach would combine work identifiers, content fingerprinting and associated metadata, complementing rather than replacing website-level opt-outs and existing sector-specific systems.
The Commission concludes that an EU-level registry could help copyright holders communicate reservations against the use of their works for text and data mining, including AI-model training, while enabling developers to identify those reservations more consistently.
The significance is that European copyright policy may be moving from reliance on fragmented machine-readable notices toward centralized and auditable rights-management infrastructure, although the Commission has not yet committed to establishing the registry.
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digital-strategy.ec.europa.eu
The European Commission has issued operational guidance for Article 50 of the AI Act before its transparency obligations begin applying on August 2, 2026. Providers must disclose when people are interacting with systems such as chatbots, agents, or avatars and make generative outputs detectable through machine-readable marking; deployers must disclose emotion-recognition or biometric-categorization uses and clearly label deepfakes and certain AI-generated public-interest text when it lacks human review or editorial control.
The NUS/Harbin-led team identifies a domain-specific form of generative-AI hallucination: molecular candidates can receive strong predicted binding scores while violating basic chemistry or producing physically impossible atomic arrangements. Its DrugRPG framework incorporates chemical-foundation-model priors and differentiable physical constraints during molecule generation, reducing severe steric clashes by 65.4% relative to the reported state-of-the-art baseline and increasing by 28.6% the share of generated candidates meeting combined potency, stability, and synthetic-feasibility criteria.
The authors warn that AI systems can amplify stale annotations, incorrect database relationships, inconsistent standards, and weak provenance when they continuously harvest scientific repositories that were designed as comparatively static resources. They extend the FAIR principles with COPE—Comparable, Organized, Predictive, and Engaged—calling for iterative updates, version tracking, uncertainty estimates, machine-actionable standards, and community validation whenever AI-supported analyses generate new knowledge.