Add bias detection and fairness auditing for moderation models
Overview
Ensure moderation models are fair and unbiased across demographics.
Analysis Areas
- Demographic Bias: Test across age, gender, race, religion
- Linguistic Bias: AAVE, dialects, non-native speakers
- Contextual Bias: Sarcasm, cultural references
- False Positive Rates: Per demographic group
Fairness Metrics
- Demographic parity
- Equalized odds
- Calibration across groups
- Error rate balance
Testing Framework
- Synthetic test datasets
- Real-world audit samples (10K per group)
- Adversarial examples
- Red team testing
Mitigation Strategies
- Data augmentation for underrepresented groups
- Fairness constraints in training
- Post-processing calibration
- Regular audits (quarterly)
Reporting
- Public fairness dashboard
- Bias incident reporting system
- Model cards with fairness metrics
Acceptance Criteria
-
False positive rate difference <5% across groups -
Quarterly fairness audits completed -
Public model cards published -
Bias mitigation strategy documented