As computer vision systems become more widely deployed, there is increasing
concern from both the research community and the public that these systems are
not only reproducing but amplifying harmful social biases. The phenomenon of
bias amplification, which is the focus of this work, refers to models
amplifying inherent training set biases at test time. Existing metrics measure
bias amplification with respect to single annotated attributes (e.g.,
$\texttt{computer}$). However, several visual datasets consist of images with
multiple attribute annotations. We show models can learn to exploit
correlations with respect to multiple attributes (e.g., {$\texttt{computer}$,
$\texttt{keyboard}$}), which are not accounted for by current metrics. In
addition, we show current metrics can give the erroneous impression that
minimal or no bias amplification has occurred as they involve aggregating over
positive and negative values. Further, these metrics lack a clear desired
value, making them difficult to interpret. To address these shortcomings, we
propose a new metric: Multi-Attribute Bias Amplification. We validate our
proposed metric through an analysis of gender bias amplification on the COCO
and imSitu datasets. Finally, we benchmark bias mitigation methods using our
proposed metric, suggesting possible avenues for future bias mitigation

本文拟解决了计算机视觉中的社会偏见问题，主要集中于模型使用多种属性注释时会放大潜在偏见、评估指标的局限性以及提供建议的新度量标准和方法。