Sparse and Faithful Explanations Without Sparse Models
<|reference_start|>Sparse and Faithful Explanations Without Sparse Models: Even if a model is not globally sparse, it is possible for decisions made from that model to be accurately and faithfully described by a small number of features. For instance, an application for a large loan might be denied to someone because they have no credit history, which overwhelms any evidence towards their creditworthiness. In this work, we introduce the Sparse Explanation Value (SEV), a new way of measuring sparsity in machine learning models. In the loan denial example above, the SEV is 1 because only one factor is needed to explain why the loan was denied. SEV is a measure of decision sparsity rather than overall model sparsity, and we are able to show that many machine learning models -- even if they are not sparse -- actually have low decision sparsity, as measured by SEV. SEV is defined using movements over a hypercube, allowing SEV to be defined consistently over various model classes, with movement restrictions reflecting real-world constraints. We proposed the algorithms that reduce SEV without sacrificing accuracy, providing sparse and completely faithful explanations, even without globally sparse models.<|reference_end|>
@article{sun2024sparse,
    title={Sparse and Faithful Explanations Without Sparse Models},
    author={Yiyang Sun, Zhi Chen, Vittorio Orlandi, Tong Wang, Cynthia Rudin},
    journal={arXiv preprint arXiv:2402.09702},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.09702},
    primaryClass={cs.LG stat.ML}
}