Trust Regions for Explanations via Black-Box Probabilistic Certification
<|reference_start|>Trust Regions for Explanations via Black-Box Probabilistic Certification: Given the black box nature of machine learning models, a plethora of explainability methods have been developed to decipher the factors behind individual decisions. In this paper, we introduce a novel problem of black box (probabilistic) explanation certification. We ask the question: Given a black box model with only query access, an explanation for an example and a quality metric (viz. fidelity, stability), can we find the largest hypercube (i.e., $\ell_{\infty}$ ball) centered at the example such that when the explanation is applied to all examples within the hypercube, (with high probability) a quality criterion is met (viz. fidelity greater than some value)? Being able to efficiently find such a \emph{trust region} has multiple benefits: i) insight into model behavior in a \emph{region}, with a \emph{guarantee}; ii) ascertained \emph{stability} of the explanation; iii) \emph{explanation reuse}, which can save time, energy and money by not having to find explanations for every example; and iv) a possible \emph{meta-metric} to compare explanation methods. Our contributions include formalizing this problem, proposing solutions, providing theoretical guarantees for these solutions that are computable, and experimentally showing their efficacy on synthetic and real data.<|reference_end|>
@article{dhurandhar2024trust,
    title={Trust Regions for Explanations via Black-Box Probabilistic Certification},
    author={Amit Dhurandhar, Swagatam Haldar, Dennis Wei and Karthikeyan Natesan
  Ramamurthy},
    journal={arXiv preprint arXiv:2402.11168},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.11168},
    primaryClass={cs.LG cs.AI}
}