Gender Recognition or Gender Reductionism?: The Social Implications of Embedded Gender Recognition Systems

By Foad Hamidi, Morgan Klaus Scheuerman, Stacy M. Branham. 2018

260 citations recorded by the source corpus.

Subfields: Clinical Psychology, Computer Vision and Pattern Recognition, Sociology and Political Science, Safety, Robustness & Interpretability, Human-Computer Interaction

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In Human-Computer Interaction

Citations in the corpus

Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020, background): “For example, in human–computer interaction, Hamidi et al. (2018) study transgender people’s experiences with automated gender recognition systems in order to uncover how these systems reproduce structures of transgender exclusion by redefining what it means to perform gender “normally.””
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): “Although observer-ascribed gender is a poor measure of gender identity (Hamidi, Scheuerman, and Branham 2018; Lagos 2019), it can be a good measure of the gendered stereotypes about appearance that may influence GCV.”
MERLOT RESERVE: Neural Script Knowledge through Vision and Language and Sound (2022, background): “Likely some of the key features that our model learns – though we did not teach it this explicitly – involve recognizing gender, and this is harmful especially to transgender individuals [54].”
Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need? (2018, background): “e biases in relation to these definitions [3, 17, 49, 68]. In contrast, the HCI community has studied unfairness in ML systems through political, social, and psychological lenses, among others (e.g., [15, 47, 94, 108]). For example, HCI researchers have empirically studied users’ expectations and pe”
The Values Encoded in Machine Learning Research (2021, background): “This contrasts against marginalized scholars’ calls for ML models that allow for non-binaries, plural truths, contextual truths, and many ways of being [13, 18, 26].”
Characteristics of Harmful Text: Towards Rigorous Benchmarking of Language Models (2022, neutral): “In [46].”
PaLI-X: On Scaling up a Multilingual Vision and Language Model (2023, background): “We do not support the creation or application of classifiers for sensitive attributes, such as gender or ethnicity, based on visual indicators and encourage readers to delve into the comprehensive work outlining their potential risks, such as [93, 94], for further insight.”
Between Subjectivity and Imposition (2020, background): “However, previous research has shown that they may contain biases that lead to discrimination and exclusion in several domains such as credit [37], the job market [70], facial recognition systems [19, 45, 71], algorithmic ltering [4, 62], and even advertisement [1].”
REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets (2020, background): “This metric of gender label inference also brings up a larger question of which situations, if any, gender labels should ever be assigned (Scheuerman et al., 2020; Hamidi et al., 2018).”
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.”