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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Gig economy workers enjoy flexibility in choosing certain aspects of their work. Nonetheless, platform companies still need to control workers' behaviors to scale their business and ensure customers quality service. Mechanisms of control have been widely studied in traditional organizations; however, work in the gig economy differs from traditional organizations in that the role of a human supervisor is replaced with digital systems. Thus, there is reason to suspect that our traditional theories of control may not hold for new forms of work in the gig economy. To address these concerns, this study examines how gig economy workers, specifically Uber drivers, perceive behavior control and its effect on their job satisfaction. Our results suggest that emotional labor mediates the relationship between perceived behavior control and job satisfaction.