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Abstract

How fair do people perceive government decisions based on algorithmic predictions? And to what extent can the government delegate decisions to machines without sacrificing perceived procedural fairness? Using a set of vignettes in the context of predictive polic-ing, school admissions, and refugee-matching, we explore how different degrees of human-machine interaction affect fairness perceptions and procedural preferences. We imple-ment four treatments varying the extent of responsibility delegation to the machine and the degree of human involvement in the decision-making process, ranging from full human discretion, machine-based predictions with high human involvement, machine-based pre-dictions with low human involvement, and fully machine-based decisions. We find that machine-based predictions with high human involvement yield the highest and fully ma-chine-based decisions the lowest fairness scores. Different accuracy assessments can partly explain these differences. Fairness scores follow a similar pattern across contexts, with a negative level effect and lower fairness perceptions of human decisions in the con-text of predictive policing. Our results shed light on the behavioral foundations of several legal human-in-the-loop rules.