Artificial Intelligence in a Structurally Unjust Society

Abstract

Increasing concerns have been raised regarding artificial intelligence (AI) bias, and in response, efforts have been made to pursue AI fairness. In this paper, we argue that the idea of structural injustice serves as a helpful framework for clarifying the ethical concerns surrounding AI bias—including the nature of its moral problem and the responsibility for addressing it—and reconceptualizing the approach to pursuing AI fairness. Using AI in health care as a case study, we argue that AI bias is a form of structural injustice that exists when AI systems interact with other social factors to exacerbate existing social inequalities, making some groups of people more vulnerable to undeserved burdens while conferring unearned benefits to others. The goal of AI fairness, understood this way, is to pursue a more just social structure with the development and use of AI systems when appropriate. We further argue that all participating agents in the unjust social structure associated with AI bias bear a shared responsibility to join collective action with the goal of reforming the social structure, and we provide a list of practical recommendations for agents in various social positions to contribute to this collective action.

Co-design with affect stories and applied ethics for health technologies

“…The development of new digital technologies (including the so-called artificial intelligence technologies) is causing major upheavals in the medical field and raising several concerns related to practices, actors, and institutions. Examples include the privacy risk posed by monitoring and self-tracking devices (Shankar et al, 2011;Lupton, 2016;Nissenbaum and Patterson, 2016;Smit et al, 2022), the existence of biases in datasets, that are likely to discriminate against marginalized groups (DeCamp and Lindvall, 2020;Vokinger et al, 2021;Lin and Chen, 2022), and also the fear of medical dehumanization, with the computer tool coming between physicians and their patients, or even, in some cases, replacing physicians in a context of cost reduction and staff shortages (Pols and Moser, 2009;Dorsey and Ritzer, 2016).…”