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Poster
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

##### 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.