# Designing Fair AI for Managing Employees in Organizations: A Review, Critique, and Design Agenda

arXiv: https://arxiv.org/abs/2002.09054 · HTML: https://mlsanity.com/paper/2002.09054/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2020.

## Built on by (most cited first, 12 of 12)

- [A Sociotechnical View of Algorithmic Fairness](https://mlsanity.com/paper/2110.09253/) (2021)
- [Human Perceptions on Moral Responsibility of AI: A Case Study in AI-Assisted Bail Decision-Making](https://mlsanity.com/paper/2102.00625/) (2021)
- [CASS: Towards Building a Social-Support Chatbot for Online Health Community](https://mlsanity.com/paper/2101.01583/) (2021)
- [Challenges and Solutions in AI for All](https://mlsanity.com/paper/2307.10600/) (2023)
- [Human-in-the-loop Fairness: Integrating Stakeholder Feedback to Incorporate Fairness Perspectives in Responsible AI](https://mlsanity.com/paper/2312.08064/) (2023)
- [Broadening the perspective for sustainable AI: Comprehensive sustainability criteria and indicators for AI systems](https://mlsanity.com/paper/2306.13686/) (2023)
- [Explainable AI is Responsible AI: How Explainability Creates Trustworthy and Socially Responsible Artificial Intelligence](https://mlsanity.com/paper/2312.01555/) (2023)
- [Improving Fairness in Adaptive Social Exergames via Shapley Bandits](https://mlsanity.com/paper/2302.09298/) (2023)
- [Considerations for Task Allocation in Human-Robot Teams](https://mlsanity.com/paper/2210.03259/) (2022)
- [Am I Being Treated Fairly? A Conceptual Framework for Individuals to Ascertain Fairness](https://mlsanity.com/paper/2504.02461/) (2025)
- ["I think this is fair": Uncovering the Complexities of Stakeholder Decision-Making in AI Fairness Assessment](https://mlsanity.com/paper/2509.17956/) (2025)
- [Empowering Affected Individuals to Shape AI Fairness Assessments: Processes, Criteria, and Tools](https://mlsanity.com/paper/2602.06984/) (2026)

## 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.