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Assessing Heuristic Machine Learning Explanations with Model Counting.
Nina Narodytska, Aditya Shrotri, Kuldeep S. Meel, Alexey Ignatiev and Joao Marques Silva.
In Proceedings of the International Conference on Theory and Applications of Satisfiability Testing (SAT), July 2019.
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, concrete examples of which include LIME and its successor ANCHOR. Due to the heuristic nature of explanations produced by existing tools, it is necessary to validate them. In this work, we propose a SAT-based method to assess the quality of explanations produced by 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.
@inproceedings{NSMIS19,
title={Assessing Heuristic Machine Learning Explanations with Model Counting},
author={Narodytska, Nina and Shrotri, Aditya and Meel, Kuldeep S. and Ignatiev, Alexey and Marques Silva, Joao},
booktitle=SAT,
year={2019},
month=jul,
bib2html_dl_pdf={../Papers/sat19nsmis.pdf},
bib2html_pubtype={Refereed Conference},
bib2html_rescat={Formal Methods 4 ML},
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, concrete examples
of which include LIME and its successor ANCHOR. Due to the heuristic nature of explanations
produced by existing tools, it is necessary to validate them.
In this work, we propose a SAT-based method to assess the quality of explanations produced
by 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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