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Fairness Constraints: A Flexible Approach for Fair Classification

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Figure 1: Decisions of three fictitious classifiers (Cbecause it stops disproportional fractions of males and females (11, C2 and C3) on whether (1) or not (0)to stop a pedestrian on the suspicion of possessing an illegal weapon
Table 1: In addition to the overall misclassification rate, error rates can be measured intwo different ways: false negative rate and false positive rate are defined as fractions overthe class distribution in the ground truth labels, or true labels
Figure 2: [Disparate impact constraints] Performance of different (unconstrained and con-strained) classifiers along with their accuracy (Acc) and positive class acceptance rates(AR) for groups z = 0 (crosses) and z = 1 (circles)
Figure 3: [Disparate impact constraints] Decision boundaries for SVM classifier with RBFKernel trained without fairness constraints (left) and with fairness constraints (middle andright) on two synthetic datasets.Also shown are the classification accuracy (Acc) andacceptance rate (AR) for each group.
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