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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AI systems can encapsulate biases and inequalities that can have societal impact.
“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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“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).”
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“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).”
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“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…”
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“The topic of algorithmic bias has received an increasing amount of attention in the machine learning academic literature ( Wikipedia 2023a; Kirkpatrick 2016; Cowgill and Tucker 2017; Garcia 2016; Hajian et al. 2016).”
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“Moreover, algorithms can underperform in novel cases of drug side effects and underrepresented populations, possibly leading to discrimination (Garcia, 2017).”
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“Numerous cases have been identified in face recognition, gender classification, and natural language processing methods (Buolamwini and Gebru, 2018; Garcia, 2016; Wang et al., 2020; Lu et al., 2020; Costa-juss`a, 2019; Benthall and Haynes, 2019; Wilson et al., 2019).”
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“Although intended to improve objectivity and result in more evidencebased decision-making [46], the potential for bias in data-driven algorithms and systems has raised concerns in both the popular media and academic discourse [27, 40, 84].”
BackgroundAarts, S., & Wernaart, B. (2021) · Wageningen Academic Publishers eBooks · doi:10.3920/978-90-8686-922-0 · Cited by 10 · see citing works
Hashtagged Trolling and Emojified Hate against Muslims on Social Media
Al‐Rawi, A. (2022) · Religions · doi:10.3390/rel13060521 · Cited by 10 · see citing works
On the Impact of Choice Architectures on Inequality in Online Donation Platforms
Chakraborty, A., Mota, N., Biega, A. J., et al. (2019) · doi:10.1145/3308558.3313663 · Cited by 10 · see citing works
“Recently, there has been a lot of focus on biases in human behavior [7, 22, 26] as well as in algorithmic models [5, 8, 15], and their impacts on different socio-technical systems.”
BackgroundBias and Discrimination in Artificial Intelligence
Weyerer, J. C., & Langer, P. F. (2020) · Advances in e-business research series · doi:10.4018/978-1-7998-1879-3.ch011 · Cited by 11 · see citing works
Deep Water: Predicting Water Meter Failures Through a Human-Machine Intelligence Collaboration
Casini, L., Delnevo, G., Roccetti, M., et al. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-25629-6_107 · Cited by 9 · see citing works
What does it mean to provide decision support to a responsible and competent expert?
Richard, A., Mayag, B., Talbot, F., Tsoukiàs, A., & Meinard, Y. (2020) · EURO Journal on Decision Processes · doi:10.1007/s40070-020-00116-7 · Cited by 9 · see citing works
“…was based on the behaviors of physicians whose behavior can be considered to be "dangerous" or "foolish" by the rest of the medical community, the trained ML algorithm will reproduce, and even amplify, this "dangerous" behavior (Garcia, 2016; Sandvig et al., 2016; Zou and Schiebinger, 2018).”
BackgroundIntroducing Ethical Thinking About Autonomous Vehicles Into an AI Course
Furey, H., & Martin, F. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11397 · Cited by 9 · see citing works
“Face recognition systems fail to work properly across all racial groups (Garcia 2016).”
BackgroundAssessing Gender Bias in Machine Translation -- A Case Study with Google Translate
Prates, M. O. R., Avelar, P. H. C., & Lamb, L. (2018) · arXiv · doi:10.48550/arxiv.1809.02208 · Cited by 409 · see citing works
“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…”
BackgroundSuccessful implementation of the EU AI Act requires interdisciplinary efforts
Montag, C., & Finck, M. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00954-z · Cited by 9 · see citing works
Considerations for the implementation of machine learning into acute care settings
Bishara, A. M., Maze, E. H., & Maze, M. (2022) · British Medical Bulletin · doi:10.1093/bmb/ldac001 · Cited by 8 · see citing works
The Perspective of Software Professionals on Algorithmic Racism
Santos, R. D. S., de Lima, L. F. F. P., & de Magalhães, C. V. C. (2023) · Proceedings of the ... ACM-IEEE International Symposium on Empirical Software Engineering and Measurement/International Symposium on Empirical Software Engineering and Measurement · doi:10.1109/esem56168.2023.10304856 · Cited by 7 · see citing works
- 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
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Political Science and International Relations › 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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