What Constitutes a Less Discriminatory Algorithm?
Benjamin Laufer, Manish Raghavan, Solon Barocas

TL;DR
This paper explores how to define and identify less discriminatory algorithms (LDAs) that reduce disparities while meeting business needs, highlighting challenges in formalization and computational search, and proposing a reasonableness-based framework.
Contribution
It introduces a formal framework for LDAs, discusses the limitations of purely quantitative definitions, and analyzes the computational feasibility of searching for LDAs.
Findings
Formal LDA definitions face fundamental challenges without held-out data.
Purely quantitative LDA standards are insufficient; reasonableness is necessary.
Computational constraints on searching for LDAs are relatively weak.
Abstract
Disparate impact doctrine offers an important legal apparatus for targeting discriminatory data-driven algorithmic decisions. A recent body of work has focused on conceptualizing one particular construct from this doctrine: the less discriminatory alternative, an alternative policy that reduces disparities while meeting the same business needs of a status quo or baseline policy. However, attempts to operationalize this construct in the algorithmic setting must grapple with some thorny challenges and ambiguities. In this paper, we attempt to raise and resolve important questions about less discriminatory algorithms (LDAs). How should we formally define LDAs, and how does this interact with different societal goals they might serve? And how feasible is it for firms or plaintiffs to computationally search for candidate LDAs? We find that formal LDA definitions face fundamental challenges…
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Taxonomy
TopicsMetaheuristic Optimization Algorithms Research · Face and Expression Recognition
MethodsLinear Discriminant Analysis · Balanced Selection
