Gender , Race , and Power in AI

By S. Myers. 2019

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Platform aﬀordances are often driven by business considerations and interests (Van Dijck, 2013a; Salganik, 2017), and inﬂuenced by the politics, assumptions, and interests of those designing and building these platforms (Van Dijck, 2013a; West et al., 2019).”
You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings (2022, background): “However, as critiqued by Talat et al. (2021); West et al. (2019), incorporating the diversity factor may be in-195 adequate.”
Disembodied Machine Learning: On the Illusion of Objectivity in NLP (2021, background): “A lack of diversity in ML teams is often attributed as a source of socially biased technologies with corresponding calls for increasing embodying diverse experiences (West, Whittaker, and Crawford 2019).”
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI (2020, background): “For example, since there is a close relationship between workplaces with discriminatory practices and discriminatory AI tools [74], it is likely that those who are discriminated against have more incentive to trust AI tools produced by teams that represent them.”
Revolutionizing healthcare: the role of artificial intelligence in clinical practice (2023, background): “Human contribution to the design and application of AI tools is subject to bias and could be amplified by AI if not closely monitored [113].”
Towards a Standard for Identifying and Managing Bias in Artificial Intelligence (2022, background): “Yet, the AI field noticeably lacks diversity [241].”
Equity Beyond Bias in Language Technologies for Education (2019, background): “A lack of diversity on research teams is a key contributor to discriminatory outcomes of machine learning systems in practice (West et al., 2019).”
The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research (2020, background): “In contrast, scholars underscore the importance of having diversity among AI researchers to mitigate biases (Abebe, 2018; Kuhlman et al., 2020; West et al., 2019).”
Sex and gender analysis improves science and engineering (2019, background): “Beyond automotive safety systems, the importance of anthropometric characteristics, such as the carrying angle of the elbow or the shape and size of the human knee, can be used to guide sex-specific design for artificial joints, limb prostheses and occupational protective gear 50,51 .”
“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI (2021, background): “Gender distribution in our study is reflective of the AI industry’s gender disparities [126] and sampling limitations.”
Exploring Artist Gender Bias in Music Recommendation (2020, background): “Notwithstanding, recent years have seen a rise in awareness in the scientific community about the implications of socio-technical systems’ design and implementation responsible of reinforcing bias and discrimination [4, 42].”
Artificial Intelligence and Journalism (2019, background): “It is especially valuable in AI because marginalized communities are severely underrepresented in the discipline and have fewer opportunities to advocate for design that is sensitive to their identities (West, Whittaker, & Crawford, 2019).”