Address ethical AI considerations (bias, fairness, transparency)
Address bias, fairness, transparency, explainability and contestability as design requirements: measure outcomes per segment, remove proxies for protected characteristics, disclose AI involvement, produce auditable reasons, and keep qualified humans accountable for decisions about people.
Key points
- 1
Bias testing means disaggregated evaluation: compare selection rates, error rates, precision and recall across segments (gender, age, ethnicity where lawful, language or dialect, region). Aggregate accuracy can hide systematic disadvantage for a subgroup.
- 2
Look for proxies: names, postcodes, schools, photos, employment gaps and dialect can carry protected-characteristic signal even when the characteristic itself is absent. Removing one proxy (names) does not make a system fair.
- 3
An instruction such as "be fair" or "do not discriminate" is not a control and not a measurement; fairness is demonstrated with outcome data after the change.
- 4
Uniform thresholds on unequal error distributions preserve disparities; close gaps by measuring per group and tuning prompts, examples or thresholds until group error rates converge, then keep monitoring.
- 5
Transparency: Anthropic's Usage Policy requires telling end users that AI is used to help produce advice, decisions or recommendations, at minimum at the start of each session. Human-like personas without disclosure, or disclosure buried in terms of service, are not sufficient.
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High-risk domains (employment, credit, insurance, housing, healthcare, legal, academic testing) require a qualified professional to review the decision before it is finalized; AI should prepare, summarise and recommend, not decide.
- 7
Explainability: reasons must reflect the inputs actually used and be captured at decision time in structured form (factors referencing verifiable data). A post-hoc "explain your decision" call is a rationalisation; a bare score is not a reason.
- 8
Contestability: provide a route for people to challenge decisions that goes to a human, with the audit record available; explanations attached to content-moderation decisions and appeal paths are recommended practice.
- 9
Adverse actions (pausing benefits, removing content, declining credit) should not trigger automatically from an AI flag when the flag rate is unequal across groups; route to a human investigator and measure the effect of any change per segment.
- 10
Fairness across languages and user groups is a launch criterion: gate release on per-language or per-segment evaluations meeting the same threshold rather than shipping the best-served group first.
- 11
Consent screens, disclaimers and footers do not transfer responsibility away from the deployer or remove the need for human review, equal quality and explainability.
- 12
Document limitations and intended use so stakeholders and reviewers understand where the system should not be relied upon.
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