Bias risk identification
Identify the specific bias risks for the use case: protected attribute exposure, historical data imbalance, proxy variable contamination, and differential performance across counterparty or portfolio segments.
Method
Bias is not a single problem. It appears in training data, in model outputs, and in how results are applied to decisions. Effective controls address all three — not just the layer that is easiest to measure.
Evaluation set stratification
Design evaluation sets that include representative samples across relevant segments — ensuring test performance is measured across the full distribution, not just the most common cases.
Disparate impact testing
Run structured tests to detect whether model outputs produce systematically different results across demographic, geographic, or counterparty groups — using both statistical tests and business outcome analysis.
Mitigation and control design
For identified bias issues, design the appropriate control: data rebalancing, output post-processing, threshold adjustment, human review overlay, or use case restriction until the issue is resolved.
Ongoing monitoring integration
Integrate bias metrics into the production monitoring framework so disparate impact drift is detected over time — not only tested at launch.
Outputs
Artifacts produced by the process
Bias risk register
Documented inventory of identified bias risks by type, severity, and affected segment.
- Bias risk type and mechanism
- Affected segment or attribute
- Severity and regulatory relevance assessment
Stratified evaluation report
Performance comparison across all relevant segments with statistical significance testing.
- Segment-level performance metrics
- Disparate impact ratios and thresholds
- Statistical significance and sample sizes
Bias mitigation plan
Documented controls selected for each identified bias issue with rationale and expected impact.
- Mitigation approach per risk type
- Implementation timeline and owner
- Residual risk after mitigation
Ongoing bias monitoring spec
Production monitoring configuration for detecting disparate impact drift over time.
- Bias metrics tracked in production
- Alert thresholds per segment
- Review cadence and response protocol
Engagement Cadence
How the process runs in practice
Typical timeline: 2-3 weeks per release cycle
- Week 1: bias risk identification and evaluation set stratification
- Week 2: disparate impact testing and results analysis
- Week 3: mitigation design, monitoring integration, and release documentation
Output: a repeatable bias testing process embedded in the release cycle — with documented controls, monitoring, and residual risk assessments for every deployment.