Bots, Bias and Big Data: Artificial Intelligence, Algorithmic Bias and Disparate Impact Liability in Hiring Practices
Open accessAbstract
This discussion is divided into three basic parts. Part one will provide a brief overview of artificial intelligence technology, its societal implications, and use emerging uses in hiring. Part two will discuss the potential for Title VII disparate impact arising from the use of artificial intelligence in hiring. Finally, part three will discuss proposed solutions to the challenges associated with the use of artificial intelligence technology, ultimately advocating for an approach that involves careful selection of the artificial intelligence program and balancing the use of artificial intelligence technology with human intuition.
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Artificial intelligence can automate the recruiting and selecting process, which can remove unconscious human bias that affects the hiring process.
“Artificial intelligence has become a part of the recruitment industry to automate the recruiting and selecting process, which can remove unconscious human bias that affects the hiring process (Raub, 2018).”
From Ethics and discrimination in artificial intelligence-enabled recruitment practices (2023) · cites this paper as Raub, 2018
The ‘Artificial Intelligence in Hiring’ section describes bots that automate applicant screening and a system designed to reduce bias by bypassing résumé review. But the ‘Partnership with People’ section says whether an algorithm can actually be bias-free remains to be seen. The paper supports automation and bias reduction as a goal, not the claim that AI can remove unconscious human bias. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAlgorithms and AI can result in unfair employment opportunities and the potential for discrimination without accountability.
“however, algorithms and AI can result in unfair employment opportunities and the potential for discrimination without accountability.”
From Ethics and discrimination in artificial intelligence-enabled recruitment practices (2023) · cites this paper as Raub, 2018
AI-supported recruitment can reduce costs.
“Beattie et al. (2012), Newell (2015), Raub (2018), Miasato and Silva (2019), Beneduce (2020), and Johnson et al. (2020) state that it can reduce costs”
From Ethics and discrimination in artificial intelligence-enabled recruitment practices (2023) · cites this paper as Raub (2018)
The section “Artificial Intelligence in Hiring” describes automated candidate screening, and “Proposed Solution: A Balanced Approach” mentions potential efficiency gains. Neither passage says AI-supported recruitment reduces costs; searches for hiring costs, savings, and cheaper recruitment found no such statement in our copy. Page numbers were not available. (LLM, conf=0.72)
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Research works printed but not linked: 6
- Roger Parloff, The Deep-Learning Revolution, TIME (SPECIAL EDITION), Sept. 29, 2017, at 10, 14.
- Solon Barocas & Andrew D. Selbst, Big Data's Disparate Impact, 104 CALIF. L. REV. 671, 677 (2016). 33. Id. at 678. 34. Id. 35. Id. at 680. 189. Albemarle Paper Co. v. Moody, 422 U.S. 405, 425 (1975) (quoting
- McDonnell Douglas Corp. v. Green, 411 U.S. 792, 801 (1973)). 190. See Barocas & Selbst, supra note 32, at 706. 191. Infra Part III.A. 192. Zimmer, supra note 186, at 477-78. 193. See Barocas & Selbst, supra note 32, at 706. 194. Id. at 706 195. See Kim, supra note 128, at 921. 196. Barocas & Selbst, supra note 32, at 710. 197. Id. 198. Id. 205. Id. at 563. 206. Ricci, 557 U.S. at 606. 207. Id. at 609 (Ginsburg, J., dissenting). 208. Id. at 624-25 (
- Ginsburg, J., dissenting). 209. Id. at 607 (Alito J., concurring). 210. Kim, supra note 128, at 930. 211. Id. 212. Id. 213. See Pauline T. Kim, Auditing Algorithms for Discrimination 166 U. PA. L. REV. ONLINE 189, 190 (2017);
- Joshua A. Kroll et al., Accountable Algorithms, 165 U. PA. L. REV. 633, 694 (2017). 224. See Kim, supra note 213, at 190. 225. Id. at 191. 226. Id. at 197. 227. Id. at 191. 228. See id. 229. Kim, supra note 213, at 197. 230. Id. at 198. 231. Id. at 198-99. 232. Id. at 199. 239. See generally Maraschiello v. City of Buffalo Police Dep't, 709 F.3d 87, 95 (2d Cir. 2013) (holding that there was no violation under Ricci because "[u]nlike in Ricci, where the results of a specific test were simply discarded based on the racial statistics reflected in the results, here the City replaced the 2006 list with the 2008 list after spending more than a year preparing to revise its assessment methods. Its problem was with the test itself, rather than with a particular set of results" (footnote omitted));
- S.D.N.Y. 2010). 240. See supra Part II.C. 241. The Evolution of Artificial Intelligence: AI's Coming of Age, UBS, https://www.ubs.com/microsites/artificial-intelligence/en/ai-coming-age.html [https://perma.cc/WYB4-PGWY]. 242. Kim, supra note 213, at 194. 243. See generally Rodney Brooks, The Seven Deadly Sins of AI Predictions, MIT TECH REV. (Oct. 6, 2017), https://www.technologyreview.com/s/609048/the-seven-deadly- sins-of-ai-predictions/ [https://perma.cc/KG9A-SYAX]. 244. See supra Part II. 278. Omer Tene & Jules Polonetsky, Taming the Golem: Challenges of Ethical Algorithmic Decision-Making, 19 N.C. J.L. & TECH. 125, 135 (2017). 279. Id. 280. See Tessa L. Dover et al., Diversity Policies Rarely Make Companies Fairer, and They Feel Threatening to White Men, HARV. BUS. REV. (Jan. 4, 2016), https://hbr.org/2016/01/diversity-policies-dont-help-women-or-minorities-and-they-make- white-men-feel-threatened [https://perma.cc/U7N4-WASS]. 281. Id. 282. Id. 283. Id. 284. Id. 285. See generally Evan M. Roberts, Creating Stronger Diversity Initiatives in Employment Settings, CORNELL HR REV. (Nov. 4, 2011), https:// digitalcommons.ilr.cornell.edu/ cgi/ viewcontent.cgi?referer=http:/ /scholar. google.com/ &httpsredir=1&article=1026&context=chrr [https://perma.cc/X5ZK-H4R2]. 299. Julia Powles, New York City's Bold, Flawed Attempt to Make Algorithms Accountable, NEW YORKER (Dec. 20, 2017), https:// www.newyorker.com/ tech/ elements/ new-york-citys-bold-flawed-attempt-to-make-algorithms-accountable [http://perma.cc/CQ4B-2SU3]. 300. N.Y.C. COUNCIL COMM. ON TECH., REP. ON INT. NO. 1696, at 4 (Oct. 16, 2017), http://legistar.council.nyc.gov/View.ashx?M=F&ID=5505265&GUID=0BDD96E6- A15A-4A36-83BF-2FA3D5A0DCFC [https://perma.cc/DU48-84J9]. 301. Powles, supra note 299. 302. Don Sunderland, Testimony Before the New York City Council Committee on Technology (Oct. 16, 2017), (transcript available at http:// legistar .council. nyc. gov/View.ashx?M=F&ID=5522569&GUID=DFECA4F2-E157-42AB-B598- BA3A8185E3FF [https://perma.cc/FV25-KHLS]). 303. Sidney Fussell, New York City Wants to Audit the Powerful Algorithms That Control Our Lives, GIZMODO (Dec. 14, 2017, 6:00 PM), https://gizmodo.com/new-york- city-wants-to-audit-the-powerful-algorithms-th-1821305284 [https://perma.cc/ZPR4- ZGPW]. 304. Powles, supra note 299. 305. Id.
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- Authors
- Raub, M.
- Affiliations
- University of Arkansas at Fayetteville (US)
- Corresponding author
- Raub, McKenzie
- Venue
- Arkansas Law Review
- Publisher
- University of Arkansas Libraries
- Published
- 2018-12-01
- Volume
- 71
- Issue
- 2
- Pages
- 529
- ISSN
- 0004-1831 (ISSN-L)
- OpenAlex
- W2905265880
- Cited by
- 96
- References
- 0
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 19 · 2023: 32 · 2022: 12 · 2021: 9 · 2020: 9 · 2019: 3
- FWCI
- 10.44
- Type
- journal article
- Language
- en
- Open access
- Licence
- cc-by-nd
- Locations
- Arkansas law review (publishedVersion)
- Journal of the Arkansas Academy of Science (submittedVersion, cc-by-nd)
- Arkansas law review
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Social Sciences › Law › Discrimination and Equality Law
- Keywords
- artificial intelligence, algorithmic bias, indirect discrimination, Title VII, employment discrimination, hiring practices, big data, human intuition, algorithmic accountability
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