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@inproceedings{IMM24,
  title={Locally-Minimal Probabilistic Explanations},
  abstract={
    Explainable Artificial Intelligence (XAI) is widely regarding as a
    cornerstone of trustworthy AI. Unfortunately, most work on XAI offers no
    guarantees of rigor. In high-stakes domains, e.g. uses of AI that impact
    humans, the lack of rigor of explanations can have disastrous consequences.
    Formal abductive explanations offer crucial guarantees of rigor and so are
    of interest in high-stakes uses of machine learning (ML). One drawback of
    abductive explanations is explanation size, justified by the cognitive
    limits of human decision-makers. Probabilistic abductive explanations
    (PAXps) address this limitation, but their theoretical and practical
    complexity makes their exact computation most often unrealistic. This paper
    proposes novel efficient algorithms for the computation of locally-minimal
    PXAps, which offer high-quality approximations of PXAps in practice. The
    experimental results demonstrate the practical efficiency of the proposed
    algorithms.
  },
  author={
    Izza, Yacine
    and Meel, Kuldeep S.
    and Marques-Silva, Joao
  },
  booktitle=ECAI,
  year={2024},
  month=oct,
  bib2html_rescat={Formal Methods 4 ML},
  bib2html_pubtype={Refereed Conference},
  bib2html_dl_pdf={https://arxiv.org/pdf/2312.11831},
}