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Uncertainty-aware predictive modeling for fair data-driven decisions
Patrick Kaiser · Christoph Kern · David Rügamer
Event URL: https://openreview.net/forum?id=8DXj-ze0x_s »

Both industry and academia have made considerable progress in developing trustworthy and responsible machine learning (ML) systems. While critical concepts like fairness and explainability are often addressed, the safety of systems is typically not sufficiently taken into account. By viewing data-driven decision systems as socio-technical systems, we draw on the uncertainty in ML literature to show how fairML systems can also be safeML systems. We posit that a fair model needs to be an uncertainty-aware model, e.g. by drawing on distributional regression. For fair decisions, we argue that a safe fail option should be used for individuals with uncertain categorization. We introduce semi-structured deep distributional regression as a modeling framework which addresses multiple concerns brought against standard ML models and show its use in a real-world example of algorithmic profiling of job seekers.

Author Information

Patrick Kaiser (Ludwig-Maximilians-Universität München)

B.Sc. in Statistics with Minor in Philosophy (LMU Munich) - 2016 - 2019 M.Sc. in Statistics in Economic and Social Sciences (LMU Munich) - 2019 - 2022 Statistical Consultant at the Statistical Consulting Unit (LMU Munich) - 2018 - 2022 Research Assistant at the School of Social Sciences (University of Mannheim) - 2021 - 2022

Christoph Kern (University of Munich, Ludwig-Maximilians-Universität München)
David Rügamer (LMU Munich)

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