赔偿
组织公正
分配正义
经济正义
知识管理
公共关系
程序正义
互动正义
业务
社会学
心理学
计算机科学
经济
政治学
组织承诺
微观经济学
法学
神经科学
感知
作者
Lionel Robert,Casey Pierce,Liz B. Marquis,Sangmi Kim,Rasha Alahmad
标识
DOI:10.1080/07370024.2020.1735391
摘要
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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