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Fairness Als Qualitätskriterium Im Maschinellen Lernen – Rekonstruktion Des Philosophischen Konzepts Und Implikationen Für Die Nutzung Außergesetzlicher Merkmale Bei Qualifizierten Mietspiegeln

MCML Authors

Abstract

With the increased use of machine learning (ML) models within automated decision-making systems, the demands on the quality of ML models are growing. Pure prediction quality is no longer the sole quality criterion; in particular, there is an increasing demand to consider fairness aspects. This paper pursues two goals. First, it summarizes the current fairness discussion in the field of ML (fairML) and describes the most recent developments, especially with respect to the philosophical foundations of the concept of fairness within ML. On the other hand, the question is addressed to what extent so-called ‘extra-legal’ characteristics may be used in the compilation of qualified rent indices. A recent proposal by Kauermann and Windmann (AStA Wirtschafts- und Sozialstatistisches Archiv, Volume 17, 2023) on using extra-legal features in qualified rent indices includes a model-based imputation method, which we contrast with the legal requirements. Finally, we show which alternatives from the field of fairML could be used and outline the different basic philosophical assumptions behind the various methods.

article

AStA Wirtschafts- und Sozialstatistisches Archiv

18. Aug. 2024.

Authors

L. Bothmann • K. Peters

Links

DOI

Research Area

 A1 | Statistical Foundations & Explainability

BibTeXKey: BP24

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