ARTIFICIAL INTELLIGENCE, SOCIAL INEQUALITY, AND PUBLIC TRUST: EXAMINING THE IMPACT OF ALGORITHMIC DECISION-MAKING ON CONTEMPORARY SOCIETY

Abstract

The use of artificial intelligence (AI) in areas like employment, finance, education, healthcare, and public services is growing, bringing up issues of fairness, social inequality, and public trust. While the literature on algorithmic bias and the possibility for automated systems to reinforce or amplify structural inequalities has investigated the phenomena, fewer studies have focused on the ways that affected persons and stakeholders experience and interpret these processes and relate them to their perception of institutional legitimacy and trust (Barocas & Selbst, 2016; Kroll et al., 2017). The research aims to explore the role of algorithmic decision-making in the context of AI technologies and their impact on social inequalities and public trust, while focusing on aspects of fairness, transparency, accountability, and human oversight. The study adopts interpretivist/constructivist paradigm with a qualitative design, using semi-structured interviews with X number of respondents and/or qualitative document analysis. Data is analyzed using reflexive thematic analysis to reveal patterns in how participants and/or documents interpret algorithmic decision-making and its social implications. The study will also provide theoretical contribution, linking the interpretations of algorithmic fairness and inequality to the processes of public trust-building, and a practical contribution, to more transparent, accountable, participatory, and socially responsive AI governance. Its originality is in its understanding of algorithmic decision making as a sociotechnical process that is situated within the current socio-power dynamics of inequality. In summary, the study calls for the important consideration of distributive and procedural justice, transparency, accountability, and social vulnerability protection in the development of public trust in AI.