Perceived corruption reduces algorithm aversion

• DOI: 10.1002/jcpy.1373
• Publication Date: 2023-07-04T06:34:10Z
• Authors: Noah Castelo
• Keywords: Language Change; Salience (neuroscience); Inequity aversion; Injustice; Leverage (statistics)

Abstract Scholarship on when and why humans are willing to rely algorithms rather than other has made substantial progress in recent years, although virtually all such research is based Western, educated, industrialized, rich, democratic (WEIRD) participants. This limits efforts understand the cultural generalizability of attitudes toward algorithms. In this paper, I study algorithm aversion among participants from over 30 countries inhabited continents, thereby significantly increasing diversity field's knowledge base. Furthermore, leverage test a theoretically derived prediction: that perceived corruption makes algorithmic decision‐making more appealing. find who born or raised with high levels much less averse (or, some studies, not at averse), relative those low corruption. experimentally varying salience causes decrease aversion. explore mechanisms boundary conditions these effects discuss implications context can both increase injustice.