What’s in a Name? Reducing Bias in Bios without Access to Protected Attributes
Table 1: Results for the Adult dataset. Balanced TPR (i.e., per-occupation TPR, averaged over occupations), gender bias quantified as GapRMSg, race bias quantified as GapRMSr, maximum TPR gender gap, and maximum TPR race gap for different values of hyperparameter λ. Results are averaged over four runs with different random seeds.
['Method', '[ITALIC] λ', 'Balanced TPR', 'GapRMS [ITALIC] g', 'GapRMS [ITALIC] r', 'Gapmax [ITALIC] g', 'Gapmax [ITALIC] r']
The task is to predict whether an individual earns more than $50k per year (i.e., whether their occupation is “high status”). Because the dataset has a strong class imbalance, we report the balanced TPR—i.e. , we compute the per-class TPR and then average over the classes. We experiment with different values of the hyperparameter λ. When λ=0, the method is equivalent to using the conventional weighted cross-entropy loss function. Larger values of λ increase the strength of the penalty, but may lead to a less accurate classifier. Using λ=0 leads to significant gender bias: the maximum TPR gender gap is 0.303. This means that the TPR is 30% higher for men than for women. We emphasize that this does not mean that the classifier is more likely to predict that a man earns more than $50k per year, but means that the classifier is more likely to correctly predict that a man earns more than $50k per year. Both variations of our method significantly reduce race and gender biases. With CluCL, the root mean square TPR race gap is reduced from 0.12 to 0.085, while the root mean square TPR gender gap is reduced from 0.299 to 0.25. These reductions in bias result in less than one percent decrease in the balanced TPR (79.5% is decreased to 79.3%). With CoCL, the race and gender biases are further reduced: the root mean square TPR race gap is reduced to 0.08, while the root mean square TPR gender gap is reduced to 0.163, with 0.5% decrease in the balanced TPR.