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How do you evaluate bias and fairness in an LLM application?
30-second answerSay your answer out loud first, then reveal.
Methods
- Counterfactual testing: "Priya from Chennai" vs "Rahul from Delhi" vs "John from London" with the same complaint. Compare tone, helpfulness and recommended actions, using a judge plus statistical comparison.
- Slice analysis: quality metrics by language (English vs Hindi vs Tamil), dialect, user segment. Lower accuracy for some languages is a fairness issue.
- Representation in generated content: stereotypes in examples, images or recommendations.
- Decision outcomes: if the AI influences approvals (loans, claims, hiring screens), monitor outcome rates and error rates by group, in line with legal requirements and with appropriate data governance.
Mitigations
- Remove protected attributes and proxies from decision inputs where inappropriate.
- Explicit instructions and examples for neutral, respectful language.
- Improve data and evals for underperforming languages and segments.
- Keep humans in the loop for consequential decisions.
Caution: fairness definitions can conflict (equal accuracy vs equal approval rates). Choose deliberately with legal, compliance and domain stakeholders, and document the rationale.
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