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

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.

Drivers’ Age and Automated Vehicle Explanations

“…First, we demonstrated the importance of the driver's age on the ability of AV explanations to promote trust and reduce effort and anxiety. Second, in doing so, we answered numerous calls for the development of more inclusive artificial intelligence (AI) systems [16,17]. These calls highlighted the problems of AI bias.…”

“…Our results also contribute to the literature on socially inclusive AI. Many scholars have highlighted the problems of biased AI and the need to build an AI system that is more inclusive [16,17]. AI explainability has been shown to be important to the promotion of trust between humans and AI, yet to date little research has been conducted to understand how individual differences might help determine AI effectiveness.…”

“…Explanations are reasons that underlie why an action was or should be taken [8,16]. Explanations have been used to support a range of automation, such as automated decision aid, driving automation, and recommender systems.…”

Untitled

“…Better understanding patient portal user characteristics may lead to future research on patient portals that uses a more design-or user experiencebased approach, which could enable better design, technology experience, and increased adoption. It also would be interesting to see future studies that explore using artificial intelligence (AI) (Eglash et al, 2019;Robert et al, 2020a;Robert, Pierce, Marquis, Kim, & Alahmad, 2020b) to capture patients' personality traits using a design science approach, which would enable vendors to customize their systems according to patients' personalities. Researchers have suggested that privacy and trust connect closely to HIT-use behaviors.…”

Assessment of Advanced Artificial Intelligence Techniques for Streamflow Forecasting in Jhelum River Basin

“…Several (AI) based approaches are successfully applied for predicting the streamflow of rivers. These approaches comprise ANNs, SVM, SOM, ACO, PSO, GA and GEP (Babovic et al, 2000;Mehr, 2018;Robert et al, 2020).…”