Uncertainty-aware predictive modeling for fair data-driven decisions

Kaiser, Patrick ; Kern, Christoph ; Rügamer, David

Document Type: Conference presentation
Year of publication: 2022
Conference title: Trustworthy and Socially Responsible Machine Learning (TSRML), Workshop at NeurIPS 2022
Location of the conference venue: Online
Date of the conference: 09.12.2022
Related URLs:
Publication language: English
Institution: School of Social Sciences > Social Data Science and Methodology (Keusch 2022-)
Subject: 310 Statistics
Keywords (English): fairness in machine learning , uncertainty , distributional regression, safety , profiling , neural networks
Abstract: 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.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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