BALANCING CLASSIFICATION ACCURACY AND FAIRNESS OF CLASSIFIER MODELS
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United States of America
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Apr 3, 2025
app pub date -
Dec 26, 2023
filing date -
Dec 26, 2023
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Abstract
The present disclosure describes techniques for balancing classification accuracy and fairness of a model trained to perform classification tasks. At least one bias score function corresponding to each sensitive attribute associated with instances classified by the model is configured. The at least one bias score function is configured to measure fairness on an instance level. At least one modification rule is generated based on the at least one bias score function and parameters. The at least one modification rule corresponds to at least one fairness criterion. The parameters are associated with a target level of the at least one fairness criterion. At least a subset of predictions are modified by applying the at least one modification rule to the predictions generated by the model. The modified predictions satisfy the target level of the at least one fairness criterion while maintaining the classification accuracy.
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