Author
Abstract
Algorithms are intended to improve human decisions with data-driven predictions. However, algorithms provide more than just predictions to decision-makers—they often provide explicit recommendations. In this paper, I demonstrate these algorithmic recommendations have significant independent effects on human decisions. I leverage a natural experiment in which algorithmic recommendations were given to bail judges in some cases but not others. Lenient recommendations increased lenient bail decisions by 40% for marginal cases. The results are consistent with algorithmic recommendations making visible mistakes, such as violent rearrest, less costly to judges by providing them reputational cover. In this way, algorithms can affect human decisions by changing incentives, in addition to informing predictions.