Abstract
A longstanding finding in the judgment and decision-making literature is that human decision performance can be improved with the help of a mechanical aid. Despite this observation and celebrated advances in computing technologies, recently presented evidence of algorithm aversion raises concerns about whether the potential of human-machine decision-making is undermined by a human tendency to discount algorithmic outputs. In this chapter, we examine the algorithm aversion phenomenon and what it means for judgment in predictive analytics. We contextualize algorithm aversion in the broader human vs. machine debate and the augmented decision-making literature before defining algorithm aversion, its implications, and its antecedents. Finally, we conclude with proposals to improve methods and metrics to help guide the development of human-machine decision-making.
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