Fly in the Face of Bias: Algorithmic Bias in Law Enforcement’s Facial Recognition Technology and the Need for an Adaptive Legal Framework
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In 2018, Amazon's facial recognition system, Rekognition, came under scrutiny for exhibiting racial and gender biases, with significantly higher error rates for identifying darker-skinned individuals and females.
“In 2018, Amazon's facial recognition system, Rekognition, came under scrutiny for exhibiting racial and gender biases.It significantly had higher error rates for identifying darker-skinned individuals and females.”
From Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond (2024) · cites this paper as [113]
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Research works printed but not linked: 4
- Joy Buolamwini & Timnit Gebru, Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classifications, 81 PROC. MACH. LEARNING RESCH. 71, 88 (2018), http://proceedings.mlr.press/v81/buolamwini18a/ buolamwini18a.pdf [perma.cc/D24E-9JK6].
- Know It, N.Y. TIMES (Feb. 10, 2020), https://www.nytimes.com/2020/01/18/ technology/clearview-privacy-facial-recognition.html [perma.cc/7SL8-YWKC] (documenting an investigation into a company that sells FRT, which at first remained very private but eventually has been more willing to discuss their software with journalists). 195. Id. 196. See Vill. of Arlington Heights v. Metro. Hous. Dev. Corp., 429 U.S. 252, 264- 65 (1977) (noting that Washington v. Davis, 426 U.S. 229 (1976), has been repeatedly relied on to reaffirm the need for "proof of racially discriminatory intent or purpose" in a variety of contexts). 197. Desai & Kroll, supra note 20. 198. See Buolamwini & Gebru, supra note 14. 199. 517 U.S. 806, 813 (1996). 200. Id. 201. See Illinois v. Gates, 462 U.S. 213, 232 (1983) (discussing the standard of proof for finding probable cause). 202. See, e.g., Florida v. J.L., 529 U.S. 266, 270 (2000). 203. See Gates, 462 U.S. at 232. 204. See NLRB v. Catholic Bishop of Chi., 440 U.S. 490, 508-09 (1979) (
- Brennan, J., dissenting). 205. See id. at 509 (quoting Yu Cong Eng v. Trinidad, 271 U.S. 500, 518 (1926))
- mendment may not be substituted for construction, and that a court may not exercise legislative functions to save [a] law from conflict with constitutional limitation."). 206. Press Release, U.S. Sen. Cory Booker, supra note 165. 207. Press Release, U.S. Congressman Mark Takano, supra note 172. 208. See generally Mark Seidenfeld, Bending the Rules: Flexible Regulation and Constraints on Agency Discretion, 51 ADMIN. L. REV. 429 (Spring 1999). 209. Press Release, U.S. Congressman Mark Takano, supra note 172. 210. GARVIE ET AL., supra notes 17, 180-182. 211. GROTHER ET AL., supra note 43 (finding bias in the form of demographic differentials in contemporary face recognition algorithms). 212. See id. 213. GARVIE ET AL., supra note 17. 214. Justice in Forensic Algorithms Act of 2019, H.R. 4368, 116th Cong. (2019).
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Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond
Nasir, S., Khan, R. A., & Bai, S. (2024) · IEEE Access · doi:10.1109/access.2024.3369912 · Cited by 152 · see citing works
- Venue
- Minnesota Journal of Law & Inequality
- Publisher
- University of Minnesota
- Published
- 2021
- ISSN
- 0737-089X (ISSN-L)
- 2573-0037
- OpenAlex
- W4238172518
- Cited by
- 7
- References
- 0
- Citations by year
- 2025: 5 · 2024: 1 · 2023: 1
- FWCI
- 0.63
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Law & inequality (publishedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Political Science and International Relations › Artificial Intelligence in Law
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- algorithmic bias, facial recognition technology, law enforcement, predictive policing, algorithmic accountability, racial bias in policing
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