Modeling Discrimination with Causal Abstraction

TL;DR

This paper introduces a framework that models race as a high-level causal abstraction over social features, enabling precise reasoning about discrimination despite social complexity.

Contribution

It proposes a novel abstraction framework that explicitly aligns race with social features, addressing challenges of modularity and causality in discrimination modeling.

Findings

Framework clarifies when race causes discrimination

Allows normative and empirical analysis of social assumptions

Distinguishes constitutive and causal relations in discrimination

Abstract

A person is directly racially discriminated against only if her race caused her worse treatment. This implies that race is an attribute sufficiently separable from other attributes to isolate its causal role. But race is embedded in a nexus of social factors that resist isolated treatment. If race is socially constructed, in what sense can it cause worse treatment? Some propose that the perception of race, rather than race itself, causes worse treatment. Others suggest that since causal models require \textit{modularity}, i.e. the ability to isolate causal effects, attempts to causally model discrimination are misguided. This paper addresses the problem differently. We introduce a framework for reasoning about discrimination, in which race is a high-level \textit{abstraction} of lower-level features. In this framework, race can be modeled as itself causing worse treatment. Modularity is ensured by allowing assumptions about social construction to be precisely and explicitly stated, via an alignment between race and its constituents. Such assumptions can then be subjected to normative and empirical challenges, which lead to different views of when discrimination occurs. By distinguishing constitutive and causal relations, the abstraction framework pinpoints disagreements in the current literature on modeling discrimination, while preserving a precise causal account of discrimination.