  Twitter uses machine learning to crop images, where crops are centered around
the part predicted to be the most salient. In fall 2020, Twitter users raised
concerns that the automated image cropping system on Twitter favored
light-skinned over dark-skinned individuals, as well as concerns that the
system favored cropping woman's bodies instead of their heads. In order to
address these concerns, we conduct an extensive analysis using formalized group
fairness metrics. We find systematic disparities in cropping and identify
contributing factors, including the fact that the cropping based on the single
most salient point can amplify the disparities because of an effect we term
argmax bias. However, we demonstrate that formalized fairness metrics and
quantitative analysis on their own are insufficient for capturing the risk of
representational harm in automatic cropping. We suggest the removal of
saliency-based cropping in favor of a solution that better preserves user
agency. For developing a new solution that sufficiently address concerns
related to representational harm, our critique motivates a combination of
quantitative and qualitative methods that include human-centered design.
