5.5 — Ethical AI: Bias, Fairness, and Transparency

CCAR-P Domain 5 · Task 5.5

What You Need to Know

Bias is a gap in outcomes between groups. Dropping the protected attribute proves nothing — proxies carry the signal.

Bias Is Measured in Outcomes

Proxies: working hours, occupation, application channel, address history, language register. Strong aggregate accuracy can hide one cohort failing badly. Choose cohorts from a harm hypothesis, not data availability. Fairness is a documented choice of criterion (equal error rates / equal selection rates / calibration) tied to the harm — three criteria cannot generally all hold at once.

Remediation Ladder (cheapest first)

1. Fix the data — rebalance representation, examine labels
2. Fix the framing — change what the model is asked to decide; is AI the best solution?
3. Constrain the decision — score/rank rather than decide; require human adjudication
4. Monitor continuously — live metric, threshold, owner

Not rungs: hiding the score; blaming the foundation model’s training data.

Transparency: Three Obligations

1. Disclosure to the user at the point of use — tell them they are dealing with AI; for High-risk AI Recommendations, that AI produced the recommendation
2. Explainability to the affected person — outcome-based (reasoning for this decision in everyday language, human involvement, contest route), not process-based architecture description
3. Traceability to the organisation — which version, prompt, retrieved context; retained long enough to answer a challenge months later

Accountability

A named human/role owns the outputs. Automating a decision does not move accountability onto the software. Senior management signs off intended use against risk appetite.

Exam Traps

• Concluding the system is fair because no protected attribute is collected
• Recording the foundation model’s training data as the root cause and documenting the limitation
• Offering a model card / policy page / terms paragraph as the disclosure obligation
• Explaining a decision by describing the model, pipeline or architecture