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Erschienen in:
Circular (Yet Sound) Proofs Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Assessing Heuristic Machine Learning Explanations with Model Counting

verfasst von : Nina Narodytska, Aditya Shrotri, Kuldeep S. Meel, Alexey Ignatiev, Joao Marques-Silva

Erschienen in: Theory and Applications of Satisfiability Testing – SAT 2019

Verlag: Springer International Publishing

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Abstract

Machine Learning (ML) models are widely used in decision making procedures in finance, medicine, education, etc. In these areas, ML outcomes can directly affect humans, e.g. by deciding whether a person should get a loan or be released from prison. Therefore, we cannot blindly rely on black box ML models and need to explain the decisions made by them. This motivated the development of a variety of ML-explainer systems, including LIME and its successor \({\textsc {Anchor}}\). Due to the heuristic nature of explanations produced by existing tools, it is necessary to validate them. We propose a SAT-based method to assess the quality of explanations produced by \({\textsc {Anchor}}\). We encode a trained ML model and an explanation for a given prediction as a propositional formula. Then, by using a state-of-the-art approximate model counter, we estimate the quality of the provided explanation as the number of solutions supporting it.

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Vorheriges Kapitel Backing Backtracking
Nächstes Kapitel Syntax-Guided Rewrite Rule Enumeration for SMT Solvers
Fußnoten
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In the training phase, there is an additional hard tanh layer after batch normalization but it is redundant in the inference phase.
 
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Metadaten
Titel
Assessing Heuristic Machine Learning Explanations with Model Counting
verfasst von
Nina Narodytska
Aditya Shrotri
Kuldeep S. Meel
Alexey Ignatiev
Joao Marques-Silva
Copyright-Jahr
2019
Buch
Theory and Applications of Satisfiability Testing – SAT 2019

Print ISBN: 978-3-030-24257-2

Electronic ISBN: 978-3-030-24258-9

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-24258-9_19

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