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Race and Gender
Abstract
This chapter discusses the role of race and gender in artificial intelligence (AI). The rapid permeation of AI into society has not been accompanied by a thorough investigation of the sociopolitical issues that cause certain groups of people to be harmed rather than advantaged by it. For instance, recent studies have shown that commercial automated facial analysis systems have much higher error rates for dark-skinned women, while having minimal errors on light-skinned men. Moreover, a 2016 ProPublica investigation uncovered that machine learning–based tools that assess crime recidivism rates in the United States are biased against African Americans. Other studies show that natural language–processing tools trained on news articles exhibit societal biases. While many technical solutions have been proposed to alleviate bias in machine learning systems, a holistic and multifaceted approach must be taken. This includes standardization bodies determining what types of systems can be used in which scenarios, making sure that automated decision tools are created by people from diverse backgrounds, and understanding the historical and political factors that disadvantage certain groups who are subjected to these tools.
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In the citing paper
“From massive face-recognition-based surveillance and machine-learningbased decision systems predicting crime recidivism rates, to the move towards automated health diagnostic systems, argues Gebru, the rapid permeation of AI into society has not been accompanied by a thorough investigation of the harm caused to certain groups of people such as women and minorities.”
From A decolonial approach to AI in higher education teaching and learning: strategies for undoing the ethics of digital neocolonialism (2023) · cites this paper as Gebru 2020
In the citing paper
“Gebru (2020) notes that ethics has become ' the language du jour ' in the AI fi eld, yet many times it is simply a buzz word that fails to pay attention to the serious ethical and socio-political consequences of AI in people ' s lives.”
From A decolonial approach to AI in higher education teaching and learning: strategies for undoing the ethics of digital neocolonialism (2023) · cites this paper as Gebru 2020
In the citing paper
“As computation mirrors the values of its coders (Gebru 2020)”
From A decolonial approach to AI in higher education teaching and learning: strategies for undoing the ethics of digital neocolonialism (2023) · cites this paper as Gebru 2020
In the citing paper
“this is a monumental task that has to take a holistic and multifaceted approach (Gebru 2020), namely, it has to take place both at the technical level (design and development of digital technologies) and the social level (the use of digital technologies). Focusing only on one level, while ignoring the other, will not take us very far.”
From A decolonial approach to AI in higher education teaching and learning: strategies for undoing the ethics of digital neocolonialism (2023) · cites this paper as Gebru 2020
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No references could be resolved yet.
Research works printed but not linked: 7
- 2018, Artificial Unintelligence: How Computers Misunderstand the World
- Hamidi, Foad, Morgan Klaus Scheuerman, and Stacy M. Branham. “Gender Recognition or Gender Reductionism?: The Social Implications of Embedded Gender Recognition Systems.” In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 8. New York: ACM, 2018. · DOI
- 2017, Programmed Inequality: How Britain Discarded Women Technologists and Lost Its Edge in Computing
- 2018, Algorithms of Oppression: How Search Engines Reinforce Racism
- 2016, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
- 2004, Replacing the ‘View from Nowhere’: A Pragmatist-Feminist Science Classroom., Electronic Journal of Science Education
- 2019, Discriminating Systems: Gender, Race and Power in AI., AI Now Institute,
Cited by
Sorted by citations
On the Opportunities and Risks of Foundation Models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676
Zembylas, M. (2023) · Learning, Media and Technology · doi:10.1080/17439884.2021.2010094 · Cited by 142
Metadata
- Authors
- Gebru, T.
- Affiliations
- Stanford University (US)
- Corresponding author
- Gebru, Timnit
- ORCID
- Gebru, Timnit
- Venue
- The Oxford Handbook of Ethics of AI
- Publisher
- Oxford University Press
- Published
- 2020-07-09
- Pages
- 252-269
- OpenAlex
- W3041409527
- Cited by
- 111
- References
- 0
- Citations by year
- 2026: 18 · 2025: 24 · 2024: 23 · 2023: 12 · 2022: 12 · 2021: 13 · 2020: 5
- FWCI
- 44.09
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Oxford University Press eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Social Sciences › Law › Artificial Intelligence in Law
- Keywords
- race, gender, artificial intelligence, algorithmic bias, facial analysis, recidivism prediction, African Americans, algorithmic fairness, socio-political factors
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