  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 attributes. 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 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.
Code is located at https://github.com/princetonvisualai/directional-bias-amp
