Bias detectives: the researchers striving to make algorithms fair
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Unfairness, bias and discrimination repeatedly pop up as issues and have been identified as a major challenge related to the use of algorithms and automated decision-making systems, for example to make decisions related to health, employment, credit, criminal justice, and insurance.
“Unfairness ( Smith 2017 ), bias ( Courtland 2018 ) and discrimination ( Smith 2017 ) repeatedly pop up as issues and have been identified as a major challenge ( Hacker 2018 ) related to the use of algorithms and automated decision-making systems, e.g., to make decisions related to health ( Danks & London 2017 ), employment, credit, criminal justice ( Berk 2019 ), and insurance.”
From Legal and human rights issues of AI: Gaps, challenges and vulnerabilities (2020) · cites this paper as Courtland 2018
AI can be employed for predictive policing and benefit society by allowing better allocation of police staff and reducing crime rates.
“Extant research shows that AI can be employed for purposes of predictive policing (i.e., using mathematical models to forecast what crimes will happen when and where) and therefore benefit society by allowing for a better allocation of police staff and reducing crime rates (Courtland 2018).”
From Trustworthy artificial intelligence (2021) · cites this paper as Courtland 2018
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Research works printed but not linked: 2
- Chouldechova, A. Preprint at https://arxiv.org/abs/1703.00056 (2017). · arXiv
- Chouldechova, A., Putnam-Hornstein, E., Benavides-Prado, D., Fialko, O. & Vaithianathan, R. Proc. Machine Learn. Res. 81, 134-148 (2018).
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BackgroundTrustworthy artificial intelligence
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- Authors
- Courtland, R.
- Corresponding author
- Courtland, Rachel
- Venue
- Nature
- Published
- 2018-06-20
- Volume
- 558
- Issue
- 7710
- Pages
- 357-360
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2809628680
- Cited by
- 205
- References
- 1
- Citations by year
- 2026: 11 · 2025: 30 · 2024: 25 · 2023: 19 · 2022: 16 · 2021: 34 · 2020: 32 · 2019: 33 · 2018: 5
- FWCI
- 51.70
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Political Science and International Relations › Artificial Intelligence in Law
- Keywords
- algorithmic fairness, algorithmic bias, algorithmic auditing, algorithmic justice, machine learning ethics, algorithmic discrimination
- MeSH terms
- Machine Learning, Algorithms, Humans, Research Personnel, Social Justice, Truth Disclosure, Bias
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