What are the implications when a sophisticated language model such as ChatGPT fabricates a birthday, inaccurately attributes a biography, or presents a convincingly false assertion regarding a public figure? An essential read by Stephanie Rossello in the European Law Blog investigates this issue through the perspective of GDPR requirements concerning accuracy and rectification obligations. The legal, technical, and commercial ramifications of these phenomena are indeed significant.
🔍 Key issue:
If a model produces factually incorrect personal data, does this breach the GDPR? If it does, what obligations does the controller have? The post examines two possible legal remedies under EU data protection law.
- Rectify by “adding up” context—transparency warnings, caveats, or explanatory metadata to alter the understanding of generated data
- Rectify by “correcting” the data—erasing or replacing hallucinated content with verified information
đź’ˇ But....
• Adding context may not actually prevent user misunderstanding
• Correcting hallucinated data is technically hard and commercially costly
• And under Article 16 GDPR, failure to correct verifiably inaccurate data may still violate the law, regardless of technical feasibility
📉 Bottom line: LLMs’ statistical “truth” may be incompatible with the legal standard of personal data accuracy. And if fixes remain unviable, we may face a radical solution: prohibiting LLMs from generating personal data altogether.
🧠This is a critical read for privacy lawyers, AI policy teams, and anyone thinking about regulatory-compliant LLM deployment in the EU.
đź”— Full article:
Stephanie Rossello
#AIandPrivacy #GDPR #LLMs #Hallucinations #Accuracy #RightToRectification #AICompliance #ProductCounsel #TheForwardFramework
Comment, connect and follow for more commentary on product counseling and emerging technologies. 👇
