Perceived corruption reduces algorithm aversion

Abstract

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