A Technical and Normative Investigation of Social Bias Amplification

The conversation around the fairness of machine learning models is growing and evolving. In this work, we focus on the issue of bias amplification: the tendency of models trained from data containing social biases to further amplify these biases. This problem is brought about by the algorithm, on top of the level of bias already present in the data. We make two main contributions regarding its measurement. First, building off of Zhao et al. (2017), we introduce and analyze a new, decoupled metric for measuring bias amplification, $\text{BiasAmp}_{\rightarrow}, which possesses a number of attractive properties, including the ability to pinpoint the cause of bias amplification. Second, we thoroughly analyze and discuss the normative implications of this metric. We provide suggestions about its measurement by cautioning against predicting sensitive attributes, encouraging the use of confidence intervals due to fluctuations in the fairness of models across runs, and discussing what bias amplification means in the context of domains where labels either don't exist at test time or correspond to uncertain future events. Throughout this paper, we work to provide a deeply interrogative look at the technical measurement of bias amplification, guided by our normative ideas of what we want it to encompass.