Social Norm Bias: Residual Harms of Fairness-Aware Algorithms
This addresses a subtle but harmful bias in fairness-aware algorithms for affected individuals in domains like hiring, highlighting an incremental but important limitation in current methods.
The paper tackles the problem of residual algorithmic discrimination, called Social Norm Bias (SNoB), where 'fair' classifiers still favor biographies conforming to gender norms in occupation classification, showing that post-processing fairness methods fail to mitigate this bias.
Many modern machine learning algorithms mitigate bias by enforcing fairness constraints across coarsely-defined groups related to a sensitive attribute like gender or race. However, these algorithms seldom account for within-group heterogeneity and biases that may disproportionately affect some members of a group. In this work, we characterize Social Norm Bias (SNoB), a subtle but consequential type of algorithmic discrimination that may be exhibited by machine learning models, even when these systems achieve group fairness objectives. We study this issue through the lens of gender bias in occupation classification. We quantify SNoB by measuring how an algorithm's predictions are associated with conformity to inferred gender norms. When predicting if an individual belongs to a male-dominated occupation, this framework reveals that "fair" classifiers still favor biographies written in ways that align with inferred masculine norms. We compare SNoB across algorithmic fairness methods and show that it is frequently a residual bias, and post-processing approaches do not mitigate this type of bias at all.