In the evaluation of clinical AI models for algorithmic bias, what is the formal definition of 'equalized odds' regarding the model's error rates across different demographic groups?
Equalized odds is a mathematical definition of algorithmic fairness that requires a model to perform with equal accuracy for all demographic groups. Specifically, a model satisfies equalized odds if the probability of the model producing a positive prediction is independent of the group membership, conditional on the actual outcome. This means two conditions must be met simulta....
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Lara Emad Aljudaibi
“The formal definition of equalized odds requires that the model predictor Y hat and the protected demographic attribute A are conditionally independent given the true outcome Y. Regarding error rates, equalized odds requires that both the true positive rate TPR and the false positive rate FPR are equal across all demographic groups. Consequently, the model must maintain equal false negative rates FNR and equal false positive rates FPR across protected demographic attributes, ensuring equal misclassification error rates regardless of group status.”
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