Your question is Bias Implications in AI. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
Discuss the implications of bias in AI algorithms.
Explain how bias can enter through data collection, labeling, feature engineering, model training, and deployment. Describe a practical audit for protected groups, including appropriate fairness metrics, subgroup analysis, mitigation options, and the tradeoff between fairness, accuracy, and calibration. Address monitoring after deployment and distinguish correlation-based fairness analysis from causal claims.
