arXiv:2511.20934v2 Announce Type: replace-cross Abstract: Compositional explanations are a family of methods that aim to describe the spatial alignment between neurons' receptive field activations and concepts through logical rules, typically computed via a search over all possible concept combinations. Since computing the spatial alignment over the entire state space is computationally infeasible, the literature commonly adopts assumptions related to the structure of the combinations and beam search to restrict the state space. However, beam search cannot provide any theoretical guarantees of
The paper addresses a critical limitation in AI interpretability (computational infeasibility of optimal explanations) at a time when 'explainable AI' is gaining regulatory and ethical importance.
This research provides a method for guaranteed optimal compositional explanations for neurons, which is crucial for building trust and ensuring the safe deployment of increasingly complex AI systems.
The ability to provide theoretically guaranteed optimal explanations changes the landscape of AI interpretability from heuristic approaches to formally verifiable methods, enhancing reliability and potentially accelerating adoption in sensitive domains.
- · AI developers
- · AI safety researchers
- · Regulatory bodies
- · Customers of AI
- · AI systems lacking interpretability
Improved interpretability will lead to more robust and trustworthy AI models in various applications.
Enhanced trust in AI could accelerate the adoption of AI agents and autonomous systems in high-stakes environments.
Formal guarantees in explainable AI could become a standard requirement for regulatory approval across industries, potentially shaping the future of AI certification.
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Read at arXiv cs.LG