The Susceptibility of Example-Based Explainability Methods to Class Outliers
<|reference_start|>The Susceptibility of Example-Based Explainability Methods to Class Outliers: This study explores the impact of class outliers on the effectiveness of example-based explainability methods for black-box machine learning models. We reformulate existing explainability evaluation metrics, such as correctness and relevance, specifically for example-based methods, and introduce a new metric, distinguishability. Using these metrics, we highlight the shortcomings of current example-based explainability methods, including those who attempt to suppress class outliers. We conduct experiments on two datasets, a text classification dataset and an image classification dataset, and evaluate the performance of four state-of-the-art explainability methods. Our findings underscore the need for robust techniques to tackle the challenges posed by class outliers.<|reference_end|>
@article{nematov2024the,
    title={The Susceptibility of Example-Based Explainability Methods to Class
  Outliers},
    author={Ikhtiyor Nematov, Dimitris Sacharidis, Tomer Sagi, Katja Hose},
    journal={arXiv preprint arXiv:2407.20678},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.20678},
    primaryClass={cs.LG}
}