Equalized odds is a concept from the field of fairness in machine learning and statistics, particularly in the context of predictive modeling and classification tasks. It is concerned with ensuring that a model's error rates are equitable across different groups defined by protected attributes such as race, gender, or socioeconomic status. Specifically, equalized odds requires that: 1. **True Positive Rates (TPR):** The true positive rates for different groups (e.g., minority vs. majority groups) should be equal.
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