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  Fair Governance with Humans and Machines

Hermstrüwer, Y., & Langenbach, P. (2022). Fair Governance with Humans and Machines.

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 Creators:
Hermstrüwer, Yoan1, Author              
Langenbach, Pascal1, Author              
Affiliations:
1Max Planck Institute for Research on Collective Goods, Max Planck Society, ou_2173688              

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Free keywords: algorithms; predictive policing; school admissions; refugee-matching; fairness
 Abstract: How fair are government decisions based on algorithmic predictions? And to what extent can the government delegate decisions to machines without sacrificing 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 an 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.

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 Dates: 2022-05-24
 Publication Status: Published online
 Pages: -
 Publishing info: Bonn : Max Planck Institute for Research on Collective Goods, Discussion Paper 2022/4
 Table of Contents: -
 Rev. Type: -
 Identifiers: Other: 2022/04
 Degree: -

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