Fair Governance with Humans and Machines

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 policing, school admissions, and refugee-matching, we explore how different degrees of human-machine interaction affect fairness perceptions and procedural preferences. We implement 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 predictions with low human involvement, and fully machine-based decisions. We find that machine-based predictions with high human involvement yield the highest and fully machine-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 context of predictive policing. Our results shed light on the behavioral foundations of several legal human-in-the-loop rules.