Beyond Adult and COMPAS: Fairness in Multi-Class Prediction
This addresses fairness in multi-class prediction with intersectional groups, though it is incremental as it builds on existing projection-based fairness methods.
The paper tackles the problem of creating fair probabilistic classifiers for multi-class tasks by projecting a pre-trained classifier onto fairness constraints using a multiplicative post-processing method, achieving competitive accuracy-fairness trade-offs with favorable runtime on datasets up to 1M samples.
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 protected groups, and over 1M samples.