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Workshop: Responsible Decision Making in Dynamic Environments

Beyond Adult and COMPAS: Fairness in Multi-Class Prediction

Wael Alghamdi · Hsiang Hsu · Haewon Jeong · Hao Wang · Peter Winston Michalak · Shahab Asoodeh · Flavio Calmon


We produce fair probabilistic classifiers for multi-class prediction via ``projecting'' a pre-trained classifier onto the set of models that satisfy target group-fairness requirements. The new, projected model is given by post-processing the outputs of the pre-trained classifier by a multiplicative factor. We provide a parallelizable iterative algorithm for computing the projected classifier, and derive both sample complexity and convergence guarantees. Comprehensive numerical comparisons with state-of-the-art benchmarks demonstrate that our approach maintains competitive performance in terms of accuracy-fairness trade-off curves, while achieving favorable runtime on large datasets.

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