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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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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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