Title: Watch: Ethics as an engineering requirement
Course: Advanced Data Science: A Graduate Course > Section: Module 7: Data and AI ethics and governance

Watch

▶ Recorded lecture (~20 min) + slides ↓:
- Ethics as an engineering requirement, not an afterthought: harms are specified, measured, and controlled with the same rigor as latency or accuracy, and “we did not intend it” is not a control.
- What disparate impact is and how to measure it: subgroup base rates, selection rates, error rates, and the four-fifths rule as a screening heuristic rather than a legal safe harbor.
- The impossibility results: why calibration, equalized odds, and demographic parity cannot generally hold at once when base rates differ, so choosing a fairness definition is a value judgment you must justify, not compute away.
- The NIST AI RMF as connective tissue: Govern sets the culture and accountability, Map contextualizes the system and its stakeholders, Measure quantifies risk, and Manage prioritizes and acts.
- Provenance, consent, and privacy: where data came from, whether subjects agreed to this use, and why de-identification is fragile under re-identification and linkage attacks.
- Governance and monitoring as ongoing work: models drift, populations shift, and a one-time audit is not governance; documentation, thresholds, and review cadence make it durable.

“Advanced Data Science by Michelle Blomberg is licensed under CC BY-NC-SA 4.0.”