HCI Guidelines for Gender Equity and Inclusivity

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A Step Toward More Inclusive People Annotations for Fairness (2021, background): “Gender identity should only be used in situations where participants are able to self-report gender [24].”
Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development (2021, neutral): “Employing empirical methods, such as surveys, interviews, or ethnography, can ground dataset curation decisions and make datasets more contextually useful for intended stakeholders.”
Revisiting Gendered Web Forms: An Evaluation of Gender Inputs with (Non-)Binary People (2021, methodology): “…where valid genders were solely binary—“male” or “female,” “man” or ”woman” [73]; with drastic consequences for transgender and/or non-binary [82]) people even though their genders have historical roots in numerous traditions and cultures [12, 72], and are increasingly visible in modern…”
“It’s Complicated”: Negotiating Accessibility and (Mis)Representation in Image Descriptions of Race, Gender, and Disability (2021, background): “As these highly personal but contested gender labels could be included in image descriptions, we explicitly recruited screen reader users whose gender is different from that assigned to them at birth, such as those who are non-binary and transgender, to learn how gender and misrepresentation impact ”