Invisible Women: Exposing Data Bias in a World Designed for Men

By C. Perez. 2020

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Citations in the corpus

Predictors of Attitudes Toward Gay Men and Lesbian Women in 23 Countries (2020, background): “It also supports the idea that in patriarchal societies, women are largely invisible in the public sphere (Perez, 2019), especially with regard to their sexuality (Guth, 1978; Lamble, 2009).”
Exploring Artist Gender Bias in Music Recommendation (2020, background): “A common issue is the availability of data, often limited in terms of size, user information or musical information, and as in many other fields, a chronic shortage of gender-disaggregated data [35].”
Gender Bias in Machine Translation (2021, methodology): “From car crash dummies to clinical trials, we have evidence of how not accounting for gender differences brings to the creation of male-grounded products with dire consequences (Liu and Dipietro Mager, 2016; Criado-Perez, 2019), such as higher death and injury risks in vehicle crash and less effecti”
Why Does Workplace Gender Diversity Matter? Justice, Organizational Benefits, and Policy (2019, background): “Since the first-wave feminist movement, scholars and activists have drawn attention to androcentric bias across a number of domains, including medicine, science and technology, economics, and media (e.g., Criado-Perez, 2019; Fine, 2018; Waring, 1990; Women’s Media Center, 2019).”
Five sources of bias in natural language processing (2021, background): “…by demographic biases, such as unequal performance for different user groups (Tatman, 2017), mis-identification of speakers and their needs (Criado Perez, 2019) or the proliferation of harmful stereotypes (e.g., Agarwal et al., 2019; Koolen & van Cranenburgh, 2017; Kiritchenko & Moham-mad, 2018).”
Ethical Machine Learning in Healthcare (2020, background): “Models could similarly be harnessed to learn and recommend consistent rules, giving researchers a potential opportunity to de-bias current clinical care (150), measure racial disparities and mistrust in end-of-life care (82), improve known biases against minorities (127) and disadvantaged majorities”
Bridging the Gap Between Ethics and Practice (2020, background): “Commercial practices began to shift when serious problems emerged [51] from biased decisions that influenced parole granting, when hate-filled chatbots learned from malicious social media postings, and when job hiring biases were exposed [87].”