Description:

Alex Albright (Minneapolis Fed): “The Hidden Effects of Algorithmic Recommendations” 

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 50% 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. Algorithms can change human decisions by shifting incentives, in addition to directly providing new prediction information. Finally, I show there is variation across decision-makers in adherence to recommendations, which generated unintended effects across racial groups. Lenient recommendations increased Black-white gaps in lenient bail for defendants with identical algorithmic risk scores."