Ethnic Classifications in Algorithmic Fairness: Concepts, Measures and Implications in Practice

MCML Authors

Christoph Kern

Prof. Dr.

Associate

Social Data Science and AI

Abstract

We address the challenges and implications of ensuring fairness in algorithmic decision-making (ADM) practices related to ethnicity. Expanding beyond the U.S.-centric approach to race, we provide an overview of ethnic classification schemes in European countries and emphasize how the distinct approaches to ethnicity in Europe can impact fairness assessments in ADM. Drawing on large-scale German survey data, we highlight differences in ethnic disadvantage across subpopulations defined by different measures of ethnicity. We build prediction models in the labor market, health, and finance domain and investigate the fairness implications of different ethnic classification schemes across multiple prediction tasks and fairness metrics. Our results show considerable variation in fairness scores across ethnic classifications, where error disparities for the same model can be twice as large when using different operationalizations of ethnicity. We argue that ethnic classifications differ in their ability to identify ethnic disadvantage across ADM domains and advocate for context-sensitive operationalizations of ethnicity and its transparent reporting in fair machine learning (ML) applications.

inproceedings

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ACM FAccT 2024

7th ACM Conference on Fairness, Accountability, and Transparency. Rio de Janeiro, Brazil, Jun 03-06, 2024.

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Authors

S. Jaime • C. Kern

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Links

DOI

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Research Area

 C4 | Computational Social Sciences