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Beyond Adult and COMPAS: Fair Multi-Class Prediction via Information Projection
Wael Alghamdi · Hsiang Hsu · Haewon Jeong · Hao Wang · Peter Michalak · Shahab Asoodeh · Flavio Calmon

Wed Nov 30 02:00 PM -- 04:00 PM (PST) @ Hall J #536

We consider the problem of producing fair probabilistic classifiers for multi-class classification tasks. We formulate this problem in terms of ``projecting'' a pre-trained (and potentially unfair) 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. We also evaluate our method at scale on an open dataset with multiple classes, multiple intersectional groups, and over 1M samples.

Author Information

Wael Alghamdi (Harvard University)
Hsiang Hsu (Harvard University)

I am Hsiang Hsu, a Harvard Ph.D. student working with Flavio Calmon, and also a Meta Fellow. My research interests lie in promoting the interpretability of representations, improving privacy and fairness, and understanding prediction uncertainty in machine learning. I believe these are important issues in modern machine learning when trying to deploy the models in practice.

Haewon Jeong (Harvard University)
Hao Wang (MIT-IBM)
Peter Michalak (Harvard University)
Shahab Asoodeh (McMaster University)
Flavio Calmon (Harvard University)

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