Bias Identification and Mitigation Questions

Recognizing and mitigating bias in experiments, data, models, and decision processes. Candidates should be able to identify common sources of bias such as selection bias, sampling bias, temporal effects, confounding variables, and feedback loops, and propose technical and experimental mitigations such as randomization, stratification, control groups, feature auditing, fairness metrics, and monitoring for drift. The topic also covers governance and process controls to reduce bias in measurement and product decisions.