Back to Basics How Data Protection Principles Apply to AI Systems
summary
Covers how the EDPB opinion treats model parameters as potentially personal data and what technical steps (pseudonymisation, lifecycle mitigations, output filters) actually convince regulators, plus concrete lawfulness routes like the legitimate interest assessment and when consent or other grounds might apply. Shows corporate practice: an in-house AI governance program, supplier DPIAs, bias testing for payment fraud models, and why web scraping raises invisible-processing DPIAs. Discusses documentation tools such as model cards and the legal fuss over standards access.
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chapters (10)
- 0:00GDPR and AI systems
- 7:40GDPR questions for AI
- 14:10EDPB guidance on AI models
- 24:04UK AI regulation reform
- 31:53Data protection in AI research
- 37:54Risk assessments for AI under GDPR
- 42:17Lawful grounds for AI training
- 48:52Fairness in AI training and GDPR
- 56:08GDPR and AI Act standards
- 1:04:16AI compliance Q&A
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