What is Regulatory Compliance for ML Systems?

Definition
Regulatory Compliance for ML ensures systems meet legal requirements—GDPR (EU) and CCPA (California)—for collecting, processing, storing, and deleting personal data used in training and inference.
WHY ML FACES UNIQUE CHALLENGES
Traditional software stores data explicitly—compliance means finding and deleting records. ML is different: personal data influences model weights during training. Deleting source data may not remove its impact. ML also combines data across sources making consent tracking complex.
GDPR VS CCPA KEY DIFFERENCES
GDPR applies to EU residents. Requires explicit consent before processing, grants right to erasure, mandates 72-hour breach notification. Fines reach 4% of global revenue. CCPA applies to California residents. Uses opt-out model (can collect but must honor deletion), grants right to know what data is collected.
💡 Key Insight: GDPR is opt-in (no processing without consent), CCPA is opt-out (can process until user objects). This fundamentally changes ML data pipeline design.
CORE COMPLIANCE REQUIREMENTS
Data Subject Rights: Users request access, correction, or deletion spanning training sets, features, and models. Purpose Limitation: Data for one purpose cannot power another without re-consent. Data Minimization: Collect only what you need—every unnecessary field increases compliance burden.
⚠️ Key Trade-off: Compliance conflicts with ML best practices. More data improves models but minimization requires less. Design for compliance from the start—retrofitting is expensive.

💡 Key Takeaways

✓GDPR requires opt-in consent; CCPA allows opt-out after collection

✓ML faces unique challenges: data influences model weights, deletion is not straightforward

✓Core requirements: data subject rights, purpose limitation, data minimization

📌 Interview Tips

1Understand consent model differences: GDPR opt-in fundamentally changes pipeline design

2Mention purpose limitation—data for recommendations cannot power fraud detection

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