Ethical and societal frameworks for designing data-intensive systems, distilled from "Designing Data-Intensive Applications" (Kleppmann, 2nd ed) chapter 14. Covers algorithmic accountability, bias, surveillance, consent, and the data-as-liability mindset — normative guidance, not pure engineering technique.
Use this skill when:
- Building or reviewing ML decision systems (credit, hiring, criminal justice)
- Designing systems handling personal data
- Implementing GDPR/CCPA right-to-erasure
- Reviewing surveillance/tracking features
- Auditing for algorithmic bias
- Architecting consent flows
- Making product decisions involving user data
