Fair regression with demographic parity penalty is recast as optimal transport, yielding optimal maps under Wasserstein-2 and total variation penalties that work in both aware and unaware regimes.
A Convex Framework for Fair Regression
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
We introduce a flexible family of fairness regularizers for (linear and logistic) regression problems. These regularizers all enjoy convexity, permitting fast optimization, and they span the rang from notions of group fairness to strong individual fairness. By varying the weight on the fairness regularizer, we can compute the efficient frontier of the accuracy-fairness trade-off on any given dataset, and we measure the severity of this trade-off via a numerical quantity we call the Price of Fairness (PoF). The centerpiece of our results is an extensive comparative study of the PoF across six different datasets in which fairness is a primary consideration.
verdicts
UNVERDICTED 3representative citing papers
FairST combines 1D/2D/3D convolutions with fairness regularization using novel region-based and individual-based fairness gap metrics, reducing fairness gaps over 80% while improving accuracy over LSTMs, ConvLSTMs, and 3D CNNs on bike and ride share data.
Standard OLS fairness tests for deterministic pricing algorithms use invalid standard errors; corrected estimators reveal that all 34 tested Illinois auto insurers discriminate against minority zip codes.
citing papers explorer
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Geometry of Relaxed Fair Regression: A Unified Framework for Aware and Unaware Settings
Fair regression with demographic parity penalty is recast as optimal transport, yielding optimal maps under Wasserstein-2 and total variation penalties that work in both aware and unaware regimes.
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FairST: Equitable Spatial and Temporal Demand Prediction for New Mobility Systems
FairST combines 1D/2D/3D convolutions with fairness regularization using novel region-based and individual-based fairness gap metrics, reducing fairness gaps over 80% while improving accuracy over LSTMs, ConvLSTMs, and 3D CNNs on bike and ride share data.
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Fairness Testing for Algorithmic Pricing
Standard OLS fairness tests for deterministic pricing algorithms use invalid standard errors; corrected estimators reveal that all 34 tested Illinois auto insurers discriminate against minority zip codes.