Decoupled classifiers for fair and efficient Machine Learning


Cynthia Dwork, Nicole Immorlica, Adam Kalai, and Max Leiserson. 2017. “Decoupled classifiers for fair and efficient Machine Learning.” In Fairness and Transparency in Machine Learning (FATML).


When it is ethical and legal to use a sensitive attribute (such as gender or race) in machine learning systems, the question remains how to do so. We show that the naive application of machine learning algorithms using sensitive features leads to an inherent tradeoff in accuracy between groups. We provide a simple and efficient decoupling technique, that can be added on top of any black-box machine learning algorithm, to learn different classifiers for different groups. Transfer learning is used to mitigate the problem of having too little data on any one group. 

The method can apply to a range of fairness criteria. In particular, we require the application designer to specify as joint loss function that makes explicit the trade-off between fairness and accuracy. Our reduction is shown to efficiently find the minimum loss as long as the objective has a certain natural monotonicity property which may be of independent interest in the study of fairness in algorithms.

Last updated on 10/20/2017