Position Paper: Bridging the Gap Between Machine Learning and Sensitivity Analysis

• URL: http://arxiv.org/abs/2312.13234v2
• Date: Tue, 10 Sep 2024 19:36:35 GMT
• Title: Position Paper: Bridging the Gap Between Machine Learning and Sensitivity Analysis
• Authors: Christian A. Scholbeck, Julia Moosbauer, Giuseppe Casalicchio, Hoshin Gupta, Bernd Bischl, Christian Heumann,
• Abstract summary: We argue that interpretations of machine learning (ML) models can be seen as a form of sensitivity analysis (SA) We call attention to the benefits of a unified SA-based view of explanations in ML and the necessity to fully credit related work.
• Score: 9.191045750996526
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We argue that interpretations of machine learning (ML) models or the model-building process can be seen as a form of sensitivity analysis (SA), a general methodology used to explain complex systems in many fields such as environmental modeling, engineering, or economics. We address both researchers and practitioners, calling attention to the benefits of a unified SA-based view of explanations in ML and the necessity to fully credit related work. We bridge the gap between both fields by formally describing how (a) the ML process is a system suitable for SA, (b) how existing ML interpretation methods relate to this perspective, and (c) how other SA techniques could be applied to ML.

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