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Fixed Point Explainability

18 May 2025arXiv:2505.12421archive 2025-07-28

Emanuele La Malfa, Jon Vadillo, Marco Molinari, Michael Wooldridge

This paper introduces a formal notion of fixed point explanations, inspired by the "why regress" principle, to assess, through recursive applications, the stability of the interplay between a model and its explainer. Fixed point explanations satisfy properties like minimality, stability, and faithfulness, revealing hidden model behaviours and explanatory weaknesses. We define convergence conditions for several classes of explainers, from feature-based to mechanistic tools like Sparse AutoEncoders, and we report quantitative and qualitative results.

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