Designing AI Systems that Support Fairness Across Distributive, Procedural, and Interactional Justice Dimensions
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Abstract: The need for the most fair AI systems has been overemphasized as the AI influence keeps growing and critical decisions, among others, states by the healthcare, finance, and human resources sectors, are made.
AI fairness is not only about the fair distribution of results but also it involves fair processes in which decisions are made and the features of the interactions between the AI system and users.
This article uses the concepts of organizational justice as a frame to explain the ways by which the design of an AI system could become a vehicle for: distributive justice (fair distribution of resources and results); procedural justice (decision, making process that is open and impartial); and interactional justice (communication that is respectful and empathetic). The conjunction of the three dimensions that the AI system can facilitate will make it possible for the latter to be more in line with human values and hence receive more trust, legitimacy, and acceptance from the stakeholders (Colquitt et al., 2013; Binns, 2018).
This paper also refers to the various ways which include bias mitigation techniques, algorithmic transparency, and user, centric interfaces that bring fairness into the system.
Further on, the authors explain the present continuous issues (for instance, data bias and ethical tradeoffs) and recommend future research directions for enhancing just AI systems at the end of this paper (Miller, 2017; Selbst et al., 2019).
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