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Fairness in Machine Learning for National Statistical Organizations

MCML Authors

Patrick Schenk

→ Group Frauke Kreuter
Social Data Science and AI

Christoph Kern

Prof. Dr.

Associate

Social Data Science and AI

Frauke Kreuter

Prof. Dr.

Principal Investigator

Social Data Science and AI

Abstract

National statistical organizations (NSOs) increasingly draw on machine learning (ML) to improve the timeliness and cost-effectiveness of existing processes or to offer new products. Thereby, NSOs must ensure that high standards with respect to robustness, reproducibility, and accuracy are upheld. At the same time, the ML community has started to focus on 'algorithmic fairness' as a pre-condition for a safe deployment of ML models, particularly to prevent disparate social impacts in practice. However, this literature focuses on ML for data analysis and not on ML for data collection, processing, and production, that is, the main work of NSOs. We discuss how the (safe) deployment of ML by NSOs can benefit from concepts and methodology of algorithmic fairness research. First, we highlight the importance of fairness before, during, and after data processing and analysis. We then map fairness to the quality dimensions for NSOs in Yung et al.’s (Stat J IAOS 38(1):291–308;2022) QF4SA quality framework by investigating the interaction of fairness with other dimensions and argue for fairness as its own quality dimension. Furthermore, we emphasize the importance of high-quality data products and the cumulative effect of deficits along the data collection and processing pipeline as root causes of downstream fairness issues.

article SKK25