Gender shades : intersectional phenotypic and demographic evaluation of face datasets and gender classifiers

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What’s in a Name? Reducing Bias in Bios without Access to Protected Attributes (2019, background): “For example, Buolamwini (2017) showed that commercial gender classifiers have higher error rates for women with darker skin tones than for either women or people with darker skin tones overall.”
Understanding Unequal Gender Classification Accuracy from Face Images (2018, background): “In particular, since the partition in [4] is phenotypic into different skin type categories but the darkskinned people are predominantly of African descent, it may be that other features, such as hairstyle, facial structure, cosmetics or clothing are the reason for disparity, rather than skin type a”
A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspective (2022, background): “Consequently, the real-world consequences of a failed AI-enabled system can be cata-strophic, leading to, for example, discrimination (Buolamwini, 2017; Buolamwini & Gebru, 2018; Dastin, 2022; Hoffman & Podgurski, 2022; Kayser-Bril, 2020; Olteanu et al., 2019; Ruiz, 2019), and even death (Kohli &…”
Facial recognition is the plutonium of AI (2019, background): “As Buolamwini observes, “monitoring phenotypic and demographic accuracy of these systems as well as their use is necessary to protect citizen rights” [12].”