AI ethics: fairness, transparency, accountability
This course covers the ethical substance underneath AI compliance frameworks such as ISO 42001 and the EU AI Act. It explains why demographic parity, equalized odds and calibration generally cannot hold at once. It traces where bias enters a pipeline: historical data, proxy variables, feedback loops and evaluation sets. It shows how disaggregated evaluation catches subgroup failures that an aggregate hides. It separates disclosure, explainability and interpretability, then covers accountability, contestability and automation bias. It closes with the limits of ethics as process.
What you'll learn
- ✓Why demographic parity, equalized odds and calibration conflict when base rates differ
- ✓Four places bias enters a pipeline and the fix each one needs
- ✓Disaggregated evaluation with per-group thresholds as a CI check
- ✓The difference between disclosure, explainability and interpretability
- ✓Accountable roles, contestability, automation bias and per-decision records
- ✓What stays a matter of engineering judgement after every process step
The layer under the frameworks
The senior course "AI management system: ISO 42001" gave you the process: an inventory, a policy, an impact assessment, lifecycle controls, an evidence trail, a release gate, an audit. The senior course "Compliance: the EU AI Act" gave you the legal shape: risk tiers, provider and deployer roles,…
Read the full lesson free →Curriculum
7 lessonsThe layer under the frameworks
Places ethics relative to ISO 42001 and the EU AI Act, and sets out four working questions.
Fairness is not one thing
Shows with a lending example why demographic parity, equalized odds and calibration generally cannot all hold at once.
Where bias actually enters
Describes four entry points for bias: historical data, proxy variables, feedback loops and evaluation sets with shared blind spots.
Measuring fairness: disaggregated evaluation
Explains how an aggregate metric hides subgroup failures and how per-group evaluation in CI catches them.
Three kinds of transparency
Separates disclosure, explainability and interpretability by audience and technique, including the limits of post-hoc explanations.
Accountability, contestability, and the record
Covers naming accountable roles, building working appeal paths, detecting automation bias and keeping a per-decision record.
The limits of ethics as process
Examines what remains a senior engineer's judgement after every process step is completed and every artifact filed.