Benjamin Fish, Jeremy Kun, and Ádám D. Lelkes
We study the classical AdaBoost algorithm in the context of fairness. We use the Census Income Dataset as a case study. We empirically evaluate the bias and error of four variants of AdaBoost relative to an unmodified AdaBoost baseline, and study the trade-offs between reducing bias and maintaining low error. We further define a new notion of fairness and measure it for all of our methods. Our proposed method, modifying the hypothesis output by AdaBoost by shifting the decision boundary for the protected group, outperforms the state of the art for the census dataset.