  Mitigating bias in machine learning systems requires refining our
understanding of bias propagation pathways: from societal structures to
large-scale data to trained models to impact on society. In this work, we focus
on one aspect of the problem, namely bias amplification: the tendency of models
to amplify the biases present in the data they are trained on. A metric for
measuring bias amplification was introduced in the seminal work by Zhao et al.
(2017); however, as we demonstrate, this metric suffers from a number of
shortcomings including conflating different types of bias amplification and
failing to account for varying base rates of protected classes. We introduce
and analyze a new, decoupled metric for measuring bias amplification,
$\text{BiasAmp}_{\rightarrow}$ (Directional Bias Amplification). We thoroughly
analyze and discuss both the technical assumptions and 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 the limitations of what this metric captures. Throughout this
paper, we work to provide an interrogative look at the technical measurement of
bias amplification, guided by our normative ideas of what we want it to
encompass.
