Fair Allocation through Selective Information Acquisition

TL;DR

This paper introduces an efficient algorithm for selective information screening to improve resource allocation fairness and utility under uncertainty, demonstrated on synthetic and real-world data.

Contribution

It formalizes the problem of targeted information acquisition as a series of linear programs, enhancing allocation fairness and efficiency.

Findings

Improves utility through targeted screening strategies.

Reduces disparities in resource allocation across groups.

Effective on both synthetic and real-world datasets.

Abstract

Public and private institutions must often allocate scare resources under uncertainty. Banks, for example, extend credit to loan applicants based in part on their estimated likelihood of repaying a loan. But when the quality of information differs across candidates (e.g., if some applicants lack traditional credit histories), common lending strategies can lead to disparities across groups. Here we consider a setting in which decision makers -- before allocating resources -- can choose to spend some of their limited budget further screening select individuals. We present a computationally efficient algorithm for deciding whom to screen that maximizes a standard measure of social welfare. Intuitively, decision makers should screen candidates on the margin, for whom the additional information could plausibly alter the allocation. We formalize this idea by showing the problem can be reduced to solving a series of linear programs. Both on synthetic and real-world datasets, this strategy improves utility, illustrating the value of targeted information acquisition in such decisions. Further, when there is social value for distributing resources to groups for whom we have a priori poor information -- like those without credit scores -- our approach can substantially improve the allocation of limited assets.