Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency

By Kyra Yee, Uthaipon Tao Tantipongpipat, Shubhanshu Mishra. 2021

59 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Safety Research, Safety, Robustness & Interpretability, Human-Computer Interaction

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Citations in the corpus

Ethical and social risks of harm from Language Models (2021, background): “The authors described this emphasis on the female body as perpetuating the ‘male gaze, a term used for the pervasive depiction of women as sexual objects for the pleasure of and from the perspective heterosexual men’ (Yee et al., 2021), emphasis added.”
How Platform-User Power Relations Shape Algorithmic Accountability: A Case Study of Instant Loan Platforms and Financially Stressed Users in India (2022, background): “Technology providers have also taken steps towards increasing transparency due to a combination of user demands and self-imposed responsibility [19, 84, 117, 123].”
Sociotechnical Harms: Scoping a Taxonomy for Harm Reduction (2022, background): “…harms from algorithmic systems (e.g., [80, 85]), including the inequitable distribution of resources [54], hierarchical representations of people and communities [215], disparate performance based on identity [124], and entrenchment of social inequalities (e.g., [26, 74], among others.”
Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction (2022, background): “Representational harms include instances where certain social groups experience both over- and under-exposure [27], leading to unequal visibility [242].”