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Is AI recruiting (un) ethical? A human rights perspective on the use of AI for hiring
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The use of artificial intelligence (AI) technologies in organizations' recruiting and selection procedures has become commonplace in business practice; accordingly, research on AI recruiting has increased substantially in recent years. But, though various articles have highlighted the potential opportunities and ethical risks of AI recruiting, the topic has not been normatively assessed yet. We aim to fill this gap by providing an ethical analysis of AI recruiting from a human rights perspective. In doing so, we elaborate on human rights' theoretical implications for corporate use of AI-driven hiring solutions. Therefore, we analyze whether AI hiring practices inherently conflict with the concepts of validity, autonomy, nondiscrimination, privacy, and transparency, which represent the main human rights relevant in this context. Concluding that these concepts are not at odds, we then use existing legal and ethical implications to determine organizations' responsibility to enforce and realize human rights standards in the context of AI recruiting.
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“Many AI-driven recruitment tools have exhibited biases, favoring specific candidates and overlooking job requisites [307,308].”
From AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications (2023) · cites this paper as [307]
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Cites
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., et al. (2018) · Minds and Machines · doi:10.1007/s11023-018-9482-5 · Cited by 4,774 · cited 4×
“• Transparency The outcomes of AI decisions are beyond the full control of human beings, making it difficult to trace responsibilities [17].”
BackgroundInfluentialThe ethics of algorithms: Mapping the debate
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016) · Big Data & Society · doi:10.1177/2053951716679679 · Cited by 4,567 · cited 1×
“These properties, such as automated decision-making, use of historic data, access to private data [42, 43], and AI’s black-box character [44, 45], highlight potential areas of human rights violations, as they could stand in a more general—perhaps even inherent—conflict with specific human rights, as discussed in the l…”
BackgroundThe Black Box Society: The Secret Algorithms That Control Money and Information
Pasquale, F. (2015) · doi:10.4159/harvard.9780674736061 · Cited by 3,796
Kaplan, A., & Haenlein, M. (2019) · Business Horizons · doi:10.1016/j.bushor.2018.08.004 · Cited by 3,140
Algorithms at Work: The New Contested Terrain of Control
Kellogg, K. C., Valentine, M. A., & Christin, A. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0174 · Cited by 2,732
The Ethics of AI Ethics: An Evaluation of Guidelines
Hagendorff, T. (2020) · Minds and Machines · doi:10.1007/s11023-020-09517-8 · Cited by 1,911 · cited 1×
“, 70, 71] and in various frameworks [14, 17], its exact meaning remains disputed.”
BackgroundThe Ethics of AI Ethics -- An Evaluation of Guidelines
Hagendorff, T. (2019) · arXiv · doi:10.48550/arxiv.1903.03425 · Cited by 1,907 · cited 1×
“, 70, 71] and in various frameworks [14, 17], its exact meaning remains disputed.”
BackgroundAnanny, M., & Crawford, K. (2018) · New Media & Society · doi:10.1177/1461444816676645 · Cited by 1,836
Artificial Intelligence in Human Resources Management: Challenges and a Path Forward
Tambe, P., Cappelli, P., & Yakubovich, V. (2019) · California Management Review · doi:10.1177/0008125619867910 · Cited by 1,609
Lee, M. K. (2018) · Big Data & Society · doi:10.1177/2053951718756684 · Cited by 1,338
Fjeld, J., Achten, N., Hilligoss, H., Nagy, A., & Srikumar, M. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3518482 · Cited by 998
Working and organizing in the age of the learning algorithm
Faraj, S., Pachidi, S., & Sayegh, K. (2018) · Information and Organization · doi:10.1016/j.infoandorg.2018.02.005 · Cited by 929
Mitigating bias in algorithmic hiring: evaluating claims and practices
Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372828 · Cited by 845
Impact of business analytics and enterprise systems on managerial accounting
Appelbaum, D. A., Kogan, A., Vasarhelyi, M. A., & Yan, Z. (2017) · International Journal of Accounting Information Systems · doi:10.1016/j.accinf.2017.03.003 · Cited by 839
Human + Machine : Reimagining Work in the Age of AI
Daugherty, P. R. (2021) · Cited by 556
Newman, D. T., Fast, N. J., & Harmon, D. J. (2020) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2020.03.008 · Cited by 550
Köchling, A., & Wehner, M. C. (2020) · Business Research · doi:10.1007/s40685-020-00134-w · Cited by 511
Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AIPreprint
Wachter, S., Mittelstadt, B., & Russell, C. (2020) · Computer Law and Security Review · doi:10.2139/ssrn.3547922 · Cited by 448
Ethics of AI-enabled recruiting and selection: a review and research agenda
Hunkenschroer, A. L., & Luetge, C. (2022) · Journal of Business Ethics · doi:10.1007/s10551-022-05049-6 · Cited by 447
Self-citationAI-enabled recruiting: What is it and how should a manager use it?
Black, J. S., & van Esch, P. (2020) · Business Horizons · doi:10.1016/j.bushor.2019.12.001 · Cited by 403
Working for an Algorithm: Power Asymmetries and Agency in Online Work Settings
Curchod, C., Patriotta, G., Cohen, L., & Neysen, N. (2020) · Administrative Science Quarterly · doi:10.1177/0001839219867024 · Cited by 364
Why fairness cannot be automated: Bridging the gap between EU non-discrimination law and AI
Wachter, S., Mittelstadt, B., & Russell, C. (2021) · Computer Law & Security Review · doi:10.1016/j.clsr.2021.105567 · Cited by 336
The dark sides of people analytics: Reviewing the perils for organisations and employees
Giermindl, L. M., Strich, F., Christ, O., Leicht-Deobald, U., & Redzepi, A. (2022) · European Journal of Information Systems · doi:10.1080/0960085x.2021.1927213 · Cited by 331
Justice perceptions of artificial intelligence in selection
Açıkgöz, Y., Davison, H. K., Compagnone, M., & Laske, M. M. (2020) · International Journal of Selection and Assessment · doi:10.1111/ijsa.12306 · Cited by 295
An Introduction to Ethics in Robotics and AI
Bartneck, C., Lütge, C., Wagner, A. R., & Welsh, S. (2020) · SpringerBriefs in ethics · doi:10.1007/978-3-030-51110-4 · Cited by 286
Business and Human Rights: The Evolving International Agenda
Ruggie, J. G. (2007) · American Journal of International Law · doi:10.1017/s0002930000037738 · Cited by 282
Strengthening legal protection against discrimination by algorithms and artificial intelligence
Zuiderveen Borgesius, F. J. (2020) · The International Journal of Human Rights · doi:10.1080/13642987.2020.1743976 · Cited by 267
Validity in work‐based assessment: expanding our horizons
Govaerts, M., & van der Vleuten, C. P. M. (2013) · Medical Education · doi:10.1111/medu.12289 · Cited by 250
New Talent Signals: Shiny New Objects or a Brave New World?
Chamorro‐Premuzic, T., Winsborough, D., Sherman, R. A., & Hogan, R. (2016) · Industrial and Organizational Psychology · doi:10.1017/iop.2016.6 · Cited by 233
Designing for human rights in AI
Aizenberg, E., & van den Hoven, J. (2020) · Big Data & Society · doi:10.1177/2053951720949566 · Cited by 220
AI and Big Data: A blueprint for a human rights, social and ethical impact assessment
Mantelero, A. (2018) · Computer Law and Security Review · doi:10.1016/j.clsr.2018.05.017 · Cited by 203
Integrative Social Contracts Theory
Donaldson, T. J., & Dunfee, T. W. (1995) · Economics and Philosophy · doi:10.1017/s0266267100003230 · Cited by 200
Sánchez-Monedero, J., Dencik, L., & Edwards, L. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372849 · Cited by 196
Oswald, F. L., Behrend, T. S., Putka, D. J., & Sinar, E. F. (2019) · Annual Review of Organizational Psychology and Organizational Behavior · doi:10.1146/annurev-orgpsych-032117-104553 · Cited by 193
Implementations in Machine Ethics
Tolmeijer, S., Kneer, M., Sarasua, C., Christen, M., & Bernstein, A. (2020) · ACM Computing Surveys · doi:10.1145/3419633 · Cited by 188
van Esch, P., & Black, J. S. (2019) · Business Horizons · doi:10.1016/j.bushor.2019.07.004 · Cited by 186
Ethical Considerations in AI-Based Recruitment
Mujtaba, D. F., & Mahapatra, N. R. (2019) · IEEE International Symposium on Technology and Society (ISTAS) · doi:10.1109/istas48451.2019.8937920 · Cited by 178
When Ethics Travel: The Promise and Peril of Global Business Ethics
Donaldson, T. J., & Dunfee, T. W. (1999) · California Management Review · doi:10.1177/000812569904100401 · Cited by 174
Durand, R. (2003) · Strategic Management Journal · doi:10.1002/smj.339 · Cited by 173
Wettstein, F. (2015) · Journal of Human Rights · doi:10.1080/14754835.2015.1005733 · Cited by 167
The datafication of talent: how technology is advancing the science of human potential at work
Chamorro‐Premuzic, T., Akhtar, R., Winsborough, D., & Sherman, R. A. (2017) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2017.04.007 · Cited by 156
Assessment of Complex Performances: Limitations of Key Measurement Assumptions
Delandshere, G., & Petrosky, A. R. (1998) · Educational Researcher · doi:10.3102/0013189x027002014 · Cited by 136
Lantolf, J. P., & Frawley, W. (1988) · Studies in Second Language Acquisition · doi:10.1017/s0272263100007300 · Cited by 132
From human resources to human rights: Impact assessments for hiring algorithms
Yam, J., & Skorburg, J. A. (2021) · Ethics and Information Technology · doi:10.1007/s10676-021-09599-7 · Cited by 131
Algorithmic equity in the hiring of underrepresented IT job candidates
Yarger, L., Cobb Payton, F., & Neupane, B. (2019) · Online Information Review · doi:10.1108/oir-10-2018-0334 · Cited by 122
Santoro, M. A. (2000) · Cornell University Press eBooks · doi:10.7591/9781501718441 · Cited by 107
Highly Accurate, But Still Discriminatory
Köchling, A., Riazy, S., Wehner, M., & Simbeck, K. (2020) · Business & Information Systems Engineering · doi:10.1007/s12599-020-00673-w · Cited by 107
Implementations in Machine Ethics A Survey
Tolmeijer, S., Kneer, M., Sarasua, C., Christen, M., & Bernstein, A. (2020) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-197237 · Cited by 102
Predicting Personality Using Answers to Open-Ended Interview Questions
Jayaratne, M., & Jayatilleke, B. (2020) · IEEE Access · doi:10.1109/access.2020.3004002 · Cited by 101
Why a Right to an Explanation of Algorithmic Decision-Making Should Exist: A Trust-Based Approach
Kim, T. W., & Routledge, B. (2021) · Business Ethics Quarterly · doi:10.1017/beq.2021.3 · Cited by 89
Mann, M., & Matzner, T. (2019) · Big Data & Society · doi:10.1177/2053951719895805 · Cited by 88
Engineering Equity: How AI Can Help Reduce the Harm of Implicit Bias
Lin, Y.-T., Hung, T.-W., & Huang, L. T.-L. (2020) · Philosophy & Technology · doi:10.1007/s13347-020-00406-7 · Cited by 79
Recruitment in the Times of Machine Learning
Rąb, K., & Lehnervp, B. (2019) · Management Systems in Production Engineering · doi:10.1515/mspe-2019-0018 · Cited by 78
Artificial intelligence and human rights: a business ethical assessment
KRIEBITZ, A., & LÜTGE, C. (2020) · Business and Human Rights Journal · doi:10.1017/bhj.2019.28 · Cited by 76
Self-citationAchieving Ethics and Fairness in Hiring: Going Beyond the Law
Alder, G. S., & Gilbert, J. T. (2006) · Journal of Business Ethics · doi:10.1007/s10551-006-9039-z · Cited by 68
Bias in Multimodal AI: Testbed for Fair Automatic Recruitment
Peña, A., Serna, I., Morales, A., & Fiérrez, J. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00022 · Cited by 68
AI and recruiting software: Ethical and legal implications
Fernández-Martínez, C., & Fernández, A. (2020) · Paladyn J. Behav. Robotics · doi:10.1515/pjbr-2020-0030 · Cited by 66
Business and Human Rights in Historical Perspective
Santoro, M. A. (2015) · Journal of Human Rights · doi:10.1080/14754835.2015.1025945 · Cited by 57
Simbeck, K. (2019) · IBM Journal of Research and Development · doi:10.1147/jrd.2019.2915067 · Cited by 53
Vasconcelos, M., Cardonha, C., & Gonçalves, B. (2018) · Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3278721.3278751 · Cited by 38
Corporate Responsibility for Wealth Creation and Human Rights
Enderlé, G. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108913966 · Cited by 28
Implicit Bias in Predictive Data Profiling Within Recruitments
Persson, A. (2016) · IFIP advances in information and communication technology · doi:10.1007/978-3-319-55783-0_15 · Cited by 26
Evaluations: Autonomy and Artificial Intelligence: A Threat or Savior?
Lawless, W. F., & Sofge, D. (2017) · Springer eBooks · doi:10.1007/978-3-319-59719-5_13 · Cited by 20
Hunkenschroer, A. (2021) · AI Ethics Journal · doi:10.47289/aiej20210716-3 · Cited by 18
Self-citationSójka, J., & Wempe, J. F. D. B. (2000) · Springer eBooks · doi:10.1007/978-94-011-4311-0_1 · Cited by 11
Research works printed but not linked: 16
- Precht, R.D.: Künstliche Intelligenz Und Der Sinn Des Lebens. Goldmann, München (2020)
- Latonero, M.: Governing artificial intelligence. Upholding human rights & dignity. Data & Society, pp. 1–37 (2018)
- Polli, F., Dolphin, J., Kassir, S.: On the basis of brains: how neuroscience and AI advance ethical hiring. Workforce Solut. Rev. 10, 13–15 (2019)
- Baumhart, J.T.: The employer’s right to read employee e-mail: protecting property or personal prying? Labor Lawyer 8, 923–948 (1992)
- Cavico, F.J.: Invasion of privacy in the private employment sector: tortious and ethical aspects. Hous. Law Rev. 30, 1263 (1993)
- Sandeen, S.K., Mylly, U.M.: Trade secrets and the right to information: a comparative analysis of EU and us approaches to freedom of expression and whistleblowing. N. C. J. Law Technol. 21, 1 (2019)
- Van Otterlo, M.: A machine learning view on profiling. In: Hildebrandt, M., Vries, K.D. (eds.) Privacy, Due Process and the Computational Turn-Philosophers of Law Meet Philosophers of Technology, pp. 41–64. Routledge, Abingdon (2013)
- Diakopoulos, N.: Algorithmic accountability: journalistic investigation of computational power structures. Digit. J. 3, 398–415 (2015)
- Humble, K.P., Altun, D.: Artificial intelligence and the threat to human rights. J. Internet Law 24, 1–19 (2020)
- Macaskill, W.: Doing Good Better: How Effective Altruism can Help You Help Others, Do Work that Matters, and Make Smarter Choices about Giving Back. Avery, New York (2016)
- Gal, U., Jensen, T.B., Stein, M.K.: People analytics in the age of big data: an agenda for is research. In: International Conference on Information Systems, Seoul, South Korea (2017)
- Kahnemann, D.: Thinking, Fast and Slow. Farrar, Straus and Giroux, New York (2011)
- Chesterman, S.: Artificial intelligence and the problem of autonomy. Notre Dame J. Emerg. Technol. 1, 210–250 (2020)
- Van Den Hoven, J., Manders-Huits, N.: the person as risk, the person at risk. In: ETHICOMP: Living, Working And Learning Beyond Technology, Mantua, Italy, September 24–26, pp. 408–414. (2008)
- Bîgu, D., Cernea, M.-V.: Algorithmic bias in current hiring practices: an ethical examination. In: 13th International Management Conference (IMC) on Management Strategies for High Performance. 13th International Management Conference (IMC) on Management Strategies for High Performance, Bucharest, Romania, October 31–November 1 (2019)
- Desai, D.R., Kroll, J.A.: Trust but verify: a guide to algorithms and the law. Harv. J. Law Technol. 31, 1–63 (2017)
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Metadata
- Authors
- Hunkenschroer, A. L., & Kriebitz, A.
- Affiliations
- Technical University of Munich (DE)
- Corresponding author
- Hunkenschroer, Anna Lena
- Venue
- AI and Ethics
- Published
- 2022-07-25
- Volume
- 3
- Issue
- 1
- Pages
- 199-213
- ISSN
- 2730-5953 (ISSN-L)
- 2730-5961
- PMC
- PMC9309597
- OpenAlex
- W4287218595
- Cited by
- 111
- References
- 64
- Citations by year
- 2026: 39 · 2025: 35 · 2024: 25 · 2023: 10 · 2022: 2
- FWCI
- 20.88
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- AI and Ethics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- mediaTUM (Technical University of Munich) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Social Sciences › Business, Management and Accounting › Industrial relations › Legal and Labor Studies
- Keywords
- automated hiring, human rights, ethical analysis, validity, autonomy, non-discrimination, privacy, transparency, corporate social responsibility
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