The European Data Protection Board adopted guidelines clarifying how GDPR applies to web scraping for generative-AI training and fine-tuning. The guidance treats scraping as large-scale automated extraction that often occurs without individuals’ awareness, says GDPR applies when personal data are collected, stored, organized, or retrieved, and emphasizes purpose limitation, transparency, accuracy, source reliability, timestamps, validation, data minimization, and special-category-data limits.
The European Data Protection Board adopted guidelines clarifying how GDPR applies to web scraping for generative-AI training and fine-tuning.
The key value for understanding AI's impact is that EU governance is moving away from a vague “public web data” debate toward auditable data-lifecycle controls, legal-basis analysis, and no general exemption for sensitive personal data.
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European Data Protection Board
In a randomized field experiment across a chain of middle and high schools in Turkey, giving teachers a generative-AI support tool reduced students’ intrinsic motivation by 0.11 standard deviations. Average academic performance did not change, but students taught by lower-performing teachers experienced significant declines in both performance and confidence, showing that a tool that makes lesson preparation easier for teachers does not automatically improve the student experience.
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.