Beyond Algorithm Aversion: The Impact of Conventionality on Evaluation of Algorithmic and Human-Made Errors

Abstract

Prior research has found that when an algorithm makes an error, people judge it more severely than when the same mistake is made by a human. This bias, known as algorithm aversion, was investigated across two studies (N = 1199). Specifically, we explored the effect of the status quo on people’s reactions to identical mistakes made by humans and algorithms. We found significant algorithm aversion when participants were informed that the decisions described in the scenarios are conventionally made by humans. However, when participants were told that the same decisions are conventionally made by algorithms, the bias diminishes, is eliminated, or even reverses direction. This effect of varying whether the algorithm or the human is described as the convention had a particularly strong influence on recommendations of which decision maker should be used in the future. These findings suggest that the existing status quo has a consequential influence on people’s judgments of mistakes. Implications for people’s evolving relationship with algorithms and technology are discussed.