The DOI matches its Crossref record.
Racist in the Machine
Abstract
Companies and governments need to pay attention to the unconscious and institutional biases that seep into their algorithms, argues cybersecurity expert Megan Garcia. Distorted data can skew results in web searches, home loan decisions, or photo recognition software. But the combination of increased attention to the inputs, greater clarity about the properties of the code itself, and the use of crowd-level monitoring could contribute to a more equitable online world. Without careful consideration, Garcia writes, our technology will be just as racist, sexist, and xenophobic as we are.
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“Various high-profle controversies (e.g.[10,22,112]) have raised concerns over the technology's potential to both perpetuate existing societal inequalities and introduce new types of discrimination [52,67,116].”
From Out of Context: Investigating the Bias and Fairness Concerns of “Artificial Intelligence as a Service” (2023) · cites this paper as [52]
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Baker, R. S., & Hawn, A. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00285-9 · Cited by 993
“Examples of algorithmic bias cross contexts, from criminal justice (Angwin et al., 2016), to medicine (O’Reilly-Shah et al., 2020), to computer vision (Klare et al., 2012), to hiring (Garcia, 2016).”
BackgroundAlgorithmic bias: review, synthesis, and future research directions
Kordzadeh, N., & Ghasemaghaei, M. (2022) · European Journal of Information Systems · doi:10.1080/0960085x.2021.1927212 · Cited by 770
“For instance, GDPR and similar regulations that include right to explanation provisions allow individuals to demand an explanation for the output of an algorithm and exercise their rights to contest automated and machine-assisted decisions (Almada, 2019; Garcia, 2016).”
BackgroundAl’Aref, S. J., Anchouche, K., Singh, G., Slomka, P. J., Kolli, K. K., Kumar, A., Pandey, M., Maliakal, G., van Rosendael, A. R., Beecy, A. N., et al. (2019) · European Heart Journal · doi:10.1093/eurheartj/ehy404 · Cited by 534
Algorithmic Bias in Autonomous Systems
Danks, D., & London, A. J. (2017) · Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/654 · Cited by 472
Assessing gender bias in machine translation: a case study with Google Translate
Prates, M. O. R., Avelar, P. H., & Lamb, L. C. (2020) · Neural Computing and Applications · doi:10.1007/s00521-019-04144-6 · Cited by 410
“risome results in machine learning have somewhat supported Schiebinger’s view. Not only Google photos’ statistical image labeling algorithm has been found to classify dark-skinned people as gorillas (Garcia, 2016) and purportedly intelligent programs have been suggested to be negatively biased against black prisoners…”
Background
Metadata
- Authors
- Garcia, M.
- Corresponding author
- Garcia, Megan
- Venue
- World Policy Journal
- Publisher
- Duke University Press
- Published
- 2016-12-01
- Volume
- 33
- Issue
- 4
- Pages
- 111-117
- ISSN
- 0740-2775 (ISSN-L)
- 1936-0924
- OpenAlex
- W2569124586
- Cited by
- 257
- References
- 0
- Citations by year
- 2026: 8 · 2025: 30 · 2024: 26 · 2023: 34 · 2022: 55 · 2021: 32 · 2020: 37 · 2019: 27 · 2018: 6 · 2017: 2
- FWCI
- 28.49
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- World Policy Journal (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Law › Artificial Intelligence in Law
- Social Sciences › Law › Digitalization, Law, and Regulation
- Keywords
- algorithmic bias, institutional bias, implicit bias, algorithmic discrimination, algorithmic accountability, crowd monitoring, algorithmic transparency, algorithmic fairness, sexism, xenophobia, automated decision-making
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