SUMMARY
Bias, whether conscious or unconscious, can lead to discrimination and unfairness. Detecting and addressing bias in datasets and machine learning (ML) models is crucial. Accountability and accessibility play key roles in handling bias, with audit logs and user-friendly interfaces aiding in the process. Data-related bias can be detected by re-sampling and considering feature relevances. Correlated features can be identified through clustering or building ML models for each sensitive feature. Model-related bias can be addressed by assigning class weights and using model explanations. Surrogate models and streamlined actions facilitate prompt resolution of bias. Diverse teams and AI-enabled processes enhance bias detection and prevention.
