The Misgendering Machines

By Os Keyes. 2018

293 citations recorded by the source corpus.

Automatic Gender Recognition (AGR) is a subfield of facial recognition that aims to algorithmically identify the gender of individuals from photographs or videos. In wider society the technology has proposed applications in physical access control, data analytics and advertising. Within academia, it is already used in the field of Human-Computer Interaction (HCI) to analyse social media usage. Given the long-running critiques of HCI for failing to consider and include transgender (trans) perspectives in research, and the potential implications of AGR for trans people if deployed, I sought to understand how AGR and HCI understand the term "gender", and how HCI describes and deploys gender recognition technology. Using a content analysis of papers from both fields, I show that AGR consistently operationalises gender in a trans-exclusive way, and consequently carries disproportionate risk for trans people subject to it. In addition, I use the dearth of discussion of this in HCI papers that apply AGR to discuss how HCI operationalises gender, and the implications that this has for the field's research. I conclude with recommendations for alternatives to AGR, and some ideas for how HCI can work towards a more effective and trans-inclusive treatment of gender.

Subfields: Clinical Psychology, Sociology and Political Science, Natural Language Processing, AI Ethics, Fairness & Society, Human-Computer Interaction

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Citations in the corpus

Toward Gender-Inclusive Coreference Resolution (2019, background): “around gender in other fields, such as computer vision (Keyes, 2018; Hamidi et al., 2018).”
Diagnosing Gender Bias in Image Recognition Systems (2020, background): “…of men and women here, it is important to note that a smaller body of work has begun to examine the systematic exclusion of trans and nonbinary people in algorithmic image recognition systems, which relies on conceptions of sex and gender as binary, immutable, and visually legible (Keyes 2018).”
Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence (2020, background): “…2019; Buolamwini and Gebru 2018; Lum and Isaac 2016; Noble 2018; Eubanks 2018), exploitative or extractive data practices (Gray and Suri 2019), and applications of AI that dispossess the identity and resources of vulnerable populations (Green 2019; Keyes 2018; Hanna et al. 2019; Stark 2019).”
Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communities (2021, methodology): “of sex and gender categories traditionally used in the AI research literature. The assumption of ﬁxed, binary genders fails to include and properly account for non-binary identities and trans people (Keyes 2018). Incorporating such biases in the early stages of AI system design poses a substantial r”
What is the point of fairness? (2019, background): “o recognize more genders, races and objects still predispose futures where surveillance technologies may be justified for their utility for blind people while ignoring their ongoing documented misuse [12, 13]. CONCERNS RAISED THROUGH A JUSTICE LENS Unlike AI for diagnosis, computer vision to help pe”
“It’s Complicated”: Negotiating Accessibility and (Mis)Representation in Image Descriptions of Race, Gender, and Disability (2021, background): “For marginalized groups, the misclassification of identity is a form of violence and reflective of greater societal discrimination [11, 17, 50, 53, 54, 64, 99].”
This Thing Called Fairness (2019, background): “purportedly recognizing and categorizing gender) and the violence they can perpetuate [18, 65, 75]; ethnographic investigation of how data science and algorithmic work is taught, learned, and enacted in practice [116, 117]; user perceptions of “fairness” [94]; and methods to design algorithms while ”
Gender Bias in Machine Translation (2021, background): “However, these are fundamental considerations to guide the field forward and, as HCI studies show (Vorvoreanu et al., 2019), to propel the creation of gender-inclusive technology.”
Human-Computer Insurrection: Notes on an Anarchist HCI (2019, methodology): “his approach being done—in particular Alsheikh et al’s workonintimacyinArabcontexts,andAlexAhmed’swork ontrans-inclusive interactiondesign[4,9].Nonetheless,we have(asotherpapersnote)muchprogresstomake[84,123]. HCI’sdependenceonexploitativeglobalstructurescanbe seenifweexaminethepredominantculturalco”