The Pinned AUC metric hides unintended bias when class distributions vary.
problem Unintended bias in classification models.
method Examines the Pinned AUC metric and its limitations.
result Pinned AUC can obscure different types of unintended bias.
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Paper introduces metrics to detect unintended bias in text classifiers.
problem Unintended bias in machine learning classifiers.
method Threshold-agnostic metrics considering various score distribution variations across groups.
result New metrics reveal subtle unintended bias in public models.
This work optimizes model performance while ensuring fairness through AUC constraints.
problem Ensuring fairness in machine learning models, especially for protected populations.
method Formulates fairness-aware machine learning model training as AUC optimization subject to fairness constraints, solves using stochastic first-order methods.
result Demonstrates effectiveness of the approach on real-world data under different fairness metrics.