The Racist Algorithm?
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Review of The Black Box Society: The Secret Algorithms That Control Money and Information by Frank Pasquale.
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Developing XAI can slow things down early because it needs extra software development, but it can later save considerable time by finding bugs that would otherwise surface only at a late stage.
“Although developing XAI can severely impact the pace early on in the process, as it requires additional software development, the explanation may save a considerable amount of time by identifying bugs that otherwise would not be identified until a later stage.”
From Towards eXplainable Artificial Intelligence (XAI) in Tax Law: The Need for a Minimum Legal Standard (2022) · cites this paper as [105]
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- Johnson v. Transp. Agency, 480 U.S. 616, 631-32 (1987) (citations omitted). 104. See Tex. Dep't of Hous. & Cmty. Affairs v. Inclusive Cmtys. Project, Inc., 135 S. Ct. 2507, 2513 (2015). 105. Trevor Hastie et al., The Elements of Statistical Learning: Data Mining, Inference, and Prediction 222 (2d ed. 2009) ("If we are in a data-rich situation, the best approach for [model selection and model assessment] is to randomly divide the dataset into three parts: a training set, a validation set, and a test set. The training set is used to fit the models; the validation set is used to estimate prediction error for model selection; the test set is used for assessment of the generalization error of the final chosen model."). 106. Exec. Office of the President, Big Data: A Report on Algorithmic Systems, Opportunity, and Civil Rights 5-6 (2016), https://www.whitehouse.gov/sites/default/files/ microsites/ostp/2016_0504_data_discrimination.pdf [https://perma.cc/9XQT-9VYQ].
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- Authors
- Chander, A.
- Affiliations
- University of California, Davis (US)
- Corresponding author
- Chander, Anupam
- ORCID
- Chander, Anupam
- Venue
- Michigan Law Review
- Publisher
- University of Michigan Law Library
- Published
- 2017
- Volume
- 115
- Issue
- 115.6
- Pages
- 1023
- ISSN
- 0026-2234 (ISSN-L)
- 1939-8557
- OpenAlex
- W2466363251
- Cited by
- 105
- References
- 0
- Citations by year
- 2026: 3 · 2025: 19 · 2024: 16 · 2023: 20 · 2022: 13 · 2021: 10 · 2020: 12 · 2019: 8 · 2018: 3 · 2016: 1
- FWCI
- 14.41
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Michigan Law Review (publishedVersion)
- eYLS (Yale Law School) (submittedVersion)
- SSRN Electronic Journal (submittedVersion)
- Michigan Law Review
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Social Sciences › Political Science and International Relations › Artificial Intelligence in Law
- Keywords
- algorithmic racism, algorithmic discrimination, algorithmic decision-making, algorithmic transparency, algorithmic accountability, algorithmic governance, algorithmic bias
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