Designing Fair AI for Managing Employees in Organizations: A Review, Critique, and Design Agenda
Robert, Lionel + "Jr"; Pierce, Casey; Morris, Liz; Kim, Sangmi; Alahmad, Rasha
2020-02-20
Citation
Robert, L. P., Pierce, C., Marquis, E., Kim, S., Alahmad, R. (2020). Designing Fair AI for Managing Employees in Organizations: A Review, Critique, and Design Agenda, Human- Computer Interaction, accepted
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.Publisher
Human-Computer Interaction
Other DOIs
Subjects
artificial intelligence artificial intelligence fairness artificial intelligence bias artificial intelligence unfairness AI unfairness organizational justice theory AI fairness in organizations artificial intelligence in organizations organizational artificial intelligence distributive justice procedural justice interactional justice artificial intelligence design agenda artificial intelligence management Artificial Intelligence Autonomy Protecting Worker Privacy AI Accountability AI Audits and Auditability Artificial Intelligence Accountability Artificial Intelligence Audits and Auditability Equity vs. Equality algorithmic fairness algorithmic management fair algorithms algorithmic bias Transparent artificial intelligence Artificial Intelligence Explainability Artificial Intelligence Interpretability Artificial Intelligence literature review
Types
Article
Metadata
Show full item recordShowing items related by title, author, creator and subject.
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Robert, Lionel; Bansal, Gaurav; Lütge, Christoph (AIS Transactions on Human-Computer Interaction, 2020-06-30)
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Hughes, Claretha; Robert, Lionel + "Jr"; Frady, Kristin; Arroyos, Adam (Emerald Publishing Limited, 2019-07-23)
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Tarafdar, Monideepa; Teodorescu, Mike; Tanriverdi, Hüseyin; Robert, Lionel + "Jr"; Morse, Lily (ICIS 2020, 2020-09-27)
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