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Cited by 268Ethics and Social Impacts of AIAI and HR TechnologiesJob Satisfaction and Organizational Behavior

Designing fair AI for managing employees in organizations: a review, critique, and design agenda

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Robert, Lionel P., Pierce, Casey, Marquis, Liz, Kim, Sangmi, Alahmad, Rasha·2020·Human–Computer Interaction
DOI 10.1080/07370024.2020.1735391
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Abstract

Organizations are rapidly deploying artificial intelligence (AI) systems to manage their workers. However, AI has been found at times to be unfair to workers. Unfairness toward workers has been associated with decreased worker effort and increased worker turnover. To avoid such problems, AI systems must be designed to support fairness and redress instances of unfairness. Despite the attention related to AI unfairness, there has not been a theoretical and systematic approach to developing a design agenda. This paper addresses the issue in three ways. First, we introduce the organizational justice theory, three different fairness types (distributive, procedural, interactional), and the frameworks for redressing instances of unfairness (retributive justice, restorative justice). Second, we review the design literature that specifically focuses on issues of AI fairness in organizations. Third, we propose a design agenda for AI fairness in organizations that applies each of the fairness types to organizational scenarios. Then, the paper concludes with implications for future research.

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  • Most empirical research on algorithmic transparency has focused on the how, reflected in the explainability, interpretability, and observability of algorithms.

    “Most of the empirical research on algorithmic transparency has focused on the how, reflected in the explainability, interpretability and, ultimately, in the observability of algorithms (Miller, 2019;Robert et al., 2020;Shin, 2021;Shin & Park, 2019).”

    From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Robert et al., 2020

    The Transparency, Explainability, and Interpretability sections describe making AI mechanics visible and helping employees understand how AI reaches decisions. They do not establish that most empirical research focuses on these aspects, and the retrieved passages do not identify observability as a research focus. Page numbers were not available in our copy. (LLM, conf=0.86)

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Cited by

Sorted by citations

Authors
Robert, L. P., Pierce, C., Marquis, L., Kim, S., & Alahmad, R.
Affiliations
University of Michigan (US)
Corresponding author
Robert, Lionel P.
ORCID
Venue
Human–Computer Interaction
Publisher
Informa UK Limited
Published
2020-03-20
Volume
35
Issue
5-6
Pages
545-575
ISSN
  • 0737-0024 (ISSN-L)
  • 1532-7051
DOI
10.1080/07370024.2020.1735391
OpenAlex
W3007638655
Cited by
268
References
6
Citations by year
2026: 50 · 2025: 63 · 2024: 52 · 2023: 36 · 2022: 32 · 2021: 26 · 2020: 9
FWCI
35.10
Type
journal article
Language
en
Open access
Locations
  • Human-Computer Interaction (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • SSRN Electronic Journal (submittedVersion)
  • Deep Blue (University of Michigan) (submittedVersion)
  • arXiv (Cornell University)
Funders
Technische Universität München
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 › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Job Satisfaction and Organizational Behavior
Keywords
algorithmic fairness, personnel management, organizational justice, distributive justice, procedural justice, interactional justice, retributive justice, restorative justice, AI system design, worker effort, employee turnover

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