Day 1: Preparing Performance Evidence for Analysis

Performance Signals and Data Sources

• Validation checks for performance signals
• Threshold changes tested for disparate impact
• Decision records preserve evaluation rationale
• Outlier patterns reviewed prior to conclusions

Data Quality and Context

• Ownership expectations for data quality
• Escalation for contested evaluation results
• Sensitive recommendations require human review
• Appeal routes available for disputed outcomes

Evaluation Criteria and Definitions

• Performance signals around criteria and definitions
• Fairness checks across evaluation groups
• Manager overrides documented with rationale
• Evaluation data access limited by role

Day 2: Detecting Patterns and Potential Bias

Trend and Outlier Analysis

• Ownership expectations for outlier analysis
• Bias signals compared across employee groups
• Trend changes checked against operating context
• Performance measures tied to job outcomes

Bias Signals in Ratings

• Evidence requirements for bias signals
• Context considered before interpreting performance
• Model outputs treated as informing evidence
• Evaluation criteria applied consistently across roles

AI-Assisted Calibration Review

• Validation checks for AI-assisted calibration review
• Performance evidence checked for completeness
• Audit evidence informing full traceability
• Review cadence for governance controls

Day 3: Improving Fairness and Management Judgment

Evidence-Based Performance Conversations

• Application limits around evidence-based performance conversations
• Escalation paths for control failures
• Decision authority and approval thresholds
• Control testing before operational approval

Human Review and Challenge

• Control boundaries around human review
• Exceptions handled with recorded rationale
• Policy boundaries limiting unauthorized use
• Independent oversight for high-impact cases

Fair Evaluation Improvement Plan

• Ownership expectations for fair evaluation
• Governance roles across the lifecycle
• Human oversight at critical decisions
• Evidence retention for later review

• Guided exercise applying data quality to workplace cases
• Group review evaluating bias signals against evidence
• Case analysis examining evidence-based performance conversations in workplace decisions
• Simulation testing fair evaluation under operational pressure