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

Men Also Do Laundry: Multi-Attribute Bias Amplification