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Cited by 511AI and HR TechnologiesEthics and Social Impacts of AINames, Identity, and Discrimination Research

Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development

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Köchling, Alina, Wehner, Marius Claus·2020·Business Research
DOI 10.1007/s40685-020-00134-w
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Abstract

Abstract Algorithmic decision-making is becoming increasingly common as a new source of advice in HR recruitment and HR development. While firms implement algorithmic decision-making to save costs as well as increase efficiency and objectivity, algorithmic decision-making might also lead to the unfair treatment of certain groups of people, implicit discrimination, and perceived unfairness. Current knowledge about the threats of unfairness and (implicit) discrimination by algorithmic decision-making is mostly unexplored in the human resource management context. Our goal is to clarify the current state of research related to HR recruitment and HR development, identify research gaps, and provide crucial future research directions. Based on a systematic review of 36 journal articles from 2014 to 2020, we present some applications of algorithmic decision-making and evaluate the possible pitfalls in these two essential HR functions. In doing this, we inform researchers and practitioners, offer important theoretical and practical implications, and suggest fruitful avenues for future research.

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  • Models trained on historical hiring records reproduce what those records contain.

    “Models trained on historical hiring records reproduce what those records contain[31,32].”

    From Borrowed Competence: When AI-Driven HR Builds Startups’ People Capability and When It Hollows It Out (2026) · cites this paper as [32]

  • In algorithmic administrative decisions, the element of jurisdiction lies with the authority supervising and managing the algorithmic processing system used to make the decision.

    “In algorithmic administrative decisions, the element of jurisdiction lies with the authority supervising and managing the algorithmic processing system used to make the decision.”

    From Algorithmic Decision-Making in Public Administration: The Role of Artificial Intelligence Applications in Shaping Modern Administrative Law (2025) · cites this paper as [5]

    Section 6.2 (Practical implications; page unavailable) says companies should clarify accountability and identify people responsible for using algorithmic tools. The paper discusses HR decisions, but the passages found for jurisdiction, administrative decisions, and supervisory authority do not address where jurisdiction lies in an algorithmic administrative decision. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • The electronic administrative decision does not differ from the traditional administrative decision except in the means of expressing will.

    “In this regard, the electronic administrative decision does not differ from the traditional administrative decision except in the means of expressing will, and nothing more 5 .”

    From Algorithmic Decision-Making in Public Administration: The Role of Artificial Intelligence Applications in Shaping Modern Administrative Law (2025) · cites this paper as [5]

    The Introduction and §2.1 discuss algorithmic decision-making in human resources, not electronic administrative decisions. Searches for the distinction between electronic and traditional administrative decisions and for the means of expressing will found no passage addressing the claim. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • A systematic review across recruitment and development reached conclusions compatible with the finding that vendors' bias-mitigation claims are frequently unsubstantiated and thinly documented.

    “Similarly, a systematic review across recruitment and development reached compatible conclusions [32].”

    From Borrowed Competence: When AI-Driven HR Builds Startups’ People Capability and When It Hollows It Out (2026) · cites this paper as [32]

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Authors
Köchling, A., & Wehner, M. C.
Affiliations
Heinrich Heine University Düsseldorf (DE)
Corresponding author
Köchling, Alina
ORCID
Venue
Business Research
Publisher
Springer Science and Business Media LLC
Published
2020-11
Volume
13
Issue
3
Pages
795-848
ISSN
  • 1866-8658 (ISSN-L)
  • 2198-3402
  • 2198-2627
DOI
10.1007/s40685-020-00134-w
OpenAlex
W3104114204
Cited by
511
References
27
Citations by year
2026: 131 · 2025: 130 · 2024: 102 · 2023: 79 · 2022: 53 · 2021: 15 · 2019: 1
FWCI
7.90
Type
journal article
Language
en
Open access
Licence
Locations
  • BuR - Business Research (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Econstor (Econstor) (submittedVersion, cc-by)
Topics
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Sociology and Political Science › Names, Identity, and Discrimination Research
Keywords
algorithmic decision-making, algorithmic discrimination, fairness, implicit discrimination, human resource development, human resource management, perceived injustice, personnel selection

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