Abstract: As computer vision systems become more widely deployed, there is growing concern from both the research community and the public that these systems are not only reproducing but also 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 exploit correlations with multiple attributes (e.g., {$\texttt{computer}$, $\texttt{keyboard}$}), which are not accounted for by current metrics. In addition, \new{we show} current metrics can give the 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 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 efforts.