Designing Fair AI for Managing Employees in Organizations: A Review, Critique, and Design Agenda
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Abstract (arXiv)
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.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Feb 2020.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 5; 2024: 0; 2025: 2; 2026: 1.
12 papers.
- 2021A Sociotechnical View of Algorithmic Fairnesscited by 24
- 2021Human Perceptions on Moral Responsibility of AI: A Case Study in AI-Assisted Bail Decision-Makingcited by 23
- 2021CASS: Towards Building a Social-Support Chatbot for Online Health Communitycited by 8
- 2023Challenges and Solutions in AI for Allcited by 3
- 2023Human-in-the-loop Fairness: Integrating Stakeholder Feedback to Incorporate Fairness Perspectives in Responsible AIcited by 3
- 2023Broadening the perspective for sustainable AI: Comprehensive sustainability criteria and indicators for AI systemscited by 2
- 2023Explainable AI is Responsible AI: How Explainability Creates Trustworthy and Socially Responsible Artificial Intelligencecited by 2
- 2023Improving Fairness in Adaptive Social Exergames via Shapley Banditscited by 1
- 2022Considerations for Task Allocation in Human-Robot Teamscited by 0
- 2025Am I Being Treated Fairly? A Conceptual Framework for Individuals to Ascertain Fairnesscited by 0
- 2025"I think this is fair": Uncovering the Complexities of Stakeholder Decision-Making in AI Fairness Assessmentcited by 0
- 2026Empowering Affected Individuals to Shape AI Fairness Assessments: Processes, Criteria, and Toolscited by 0
- 2026Empowering Affected Individuals to Shape AI Fairness Assessments: Processes, Criteria, and Tools27 Jan 2026
- 2025"I think this is fair": Uncovering the Complexities of Stakeholder Decision-Making in AI Fairness Assessment22 Sep 2025
- 2025Am I Being Treated Fairly? A Conceptual Framework for Individuals to Ascertain Fairness3 Apr 2025
- 2023Human-in-the-loop Fairness: Integrating Stakeholder Feedback to Incorporate Fairness Perspectives in Responsible AI13 Dec 2023
- 2023Explainable AI is Responsible AI: How Explainability Creates Trustworthy and Socially Responsible Artificial Intelligence4 Dec 2023
- 2023Challenges and Solutions in AI for All20 Jul 2023
- 2023Broadening the perspective for sustainable AI: Comprehensive sustainability criteria and indicators for AI systems22 Jun 2023
- 2023Improving Fairness in Adaptive Social Exergames via Shapley Bandits18 Feb 2023
- 2022Considerations for Task Allocation in Human-Robot Teams6 Oct 2022
- 2021A Sociotechnical View of Algorithmic Fairness27 Sep 2021
- 2021Human Perceptions on Moral Responsibility of AI: A Case Study in AI-Assisted Bail Decision-Making1 Feb 2021
- 2021CASS: Towards Building a Social-Support Chatbot for Online Health Community4 Jan 2021