Perceptions of Discriminatory Decisions of Artificial Intelligence: Unpacking the Role of Individual Characteristics

TL;DR

This study explores how individual and demographic differences influence perceptions of AI bias, emphasizing the importance of digital literacy and highlighting societal disparities in understanding AI outcomes.

Contribution

It uncovers how personal characteristics and demographics shape perceptions of AI bias, revealing links between digital self-efficacy, political ideology, and trust in AI.

Findings

Digital self-efficacy boosts positive attitudes toward AI

Liberal ideologies correlate with skepticism and negative emotions

Age and income influence understanding of AI bias

Abstract

This study investigates how personal differences (digital self-efficacy, technical knowledge, belief in equality, political ideology) and demographic factors (age, education, and income) are associated with perceptions of artificial intelligence (AI) outcomes exhibiting gender and racial bias and with general attitudes towards AI. Analyses of a large-scale experiment dataset (N = 1,206) indicate that digital self-efficacy and technical knowledge are positively associated with attitudes toward AI, while liberal ideologies are negatively associated with outcome trust, higher negative emotion, and greater skepticism. Furthermore, age and income are closely connected to cognitive gaps in understanding discriminatory AI outcomes. These findings highlight the importance of promoting digital literacy skills and enhancing digital self-efficacy to maintain trust in AI and beliefs in AI usefulness and safety. The findings also suggest that the disparities in understanding problematic AI outcomes may be aligned with economic inequalities and generational gaps in society. Overall, this study sheds light on the socio-technological system in which complex interactions occur between social hierarchies, divisions, and machines that reflect and exacerbate the disparities.