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

Lionel P. Robert*, Casey Pierce, Liz Marquis, Sangmi Kim, Rasha Alahmad

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

178 Scopus citations

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.

Original languageEnglish
Pages (from-to)545-575
Number of pages31
JournalHuman-Computer Interaction
Volume35
Issue number5-6
DOIs
StatePublished - 1 Nov 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2020 Taylor & Francis Group, LLC.

Keywords

  • AI Fairness
  • HCI Design Theory
  • Intelligent UI
  • artificial Intelligence
  • human-Robot Interaction
  • social Theory
  • work Studies

ASJC Scopus subject areas

  • Applied Psychology
  • Human-Computer Interaction

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