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@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.
  },
}