Fairness, Reliability, and Data Ethics
Well-designed rubrics with specific indicators and examples minimize subjectivity. Run calibration sessions and spot-check scoring drift across assessors. Track agreement rates over time and reinforce with refresher training whenever alignment drops below your defined reliability threshold.
Fairness, Reliability, and Data Ethics
Train assessors to recognize affinity, halo, and recency effects. Blind certain artifacts where possible and diversify review panels. Review results by demographic segments to surface inequities, then refine indicators and processes to close gaps with transparent action plans.
