AI RESEARCH
The Query Channel: Information-Theoretic Limits of Masking-Based Explanations
arXiv CS.AI
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ArXi:2604.16689v1 Announce Type: new Masking-based post-hoc explanation methods, such as KernelSHAP and LIME, estimate local feature importance by querying a black-box model under randomized perturbations. This paper formulates this procedure as communication over a query channel, where the latent explanation acts as a message and each masked evaluation is a channel use. Within this framework, the complexity of the explanation is captured by the entropy of the hypothesis class, while the query interface supplies information at a rate determined by an identification capacity per query.