Information-Theoretic Analysis of Masking-Based AI Explanation Methods Reveals Fundamental Limits
Researchers formulated masking-based explanation methods like LIME and KernelSHAP as communication channels and derived fundamental limits on when these methods can reliably recover feature importance explanations. The work shows that explanation reliability depends on an identification capacity per query, analogous to channel capacity in information theory. This provides theoretical foundations for understanding when and why popular explanation methods succeed or fail.
A new theoretical framework treats masking-based post-hoc explanation methods as communication channels, where the explanation is the message and each masked model evaluation is a channel use. The researchers derive a strong converse theorem proving that if the explanation rate exceeds the channel's identification capacity, exact recovery becomes impossible regardless of the decoder used. They also prove an achievability result showing sparse maximum-likelihood decoders can achieve reliable recovery below capacity. Experiments using Monte Carlo mutual information estimation reveal regimes where information theory permits reliable explanations but standard methods like Lasso and OLS-based procedures fail. The analysis extends to practical considerations like super-pixel resolution in vision and tokenization in language models, showing how noise and nonlinear effects degrade explanation quality and create fundamental barriers to high-resolution explanations.
What's missing
The paper's own limitations and open questions include: whether the theoretical bounds are tight in practical settings, how the framework extends to global rather than local explanations, and whether alternative explanation paradigms beyond masking-based methods have different information-theoretic characterizations.
What different sources said
- arXiv cs.AICenter
The Query Channel: Information-Theoretic Limits of Masking-Based Explanations
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