Constraint-Anchored Attribution: Feasibility-Certified Counterfactuals and Bonferroni-PAC Sufficient Subsets for Neural CO Policies
arXiv:2605.25235v1 Announce Type: new Abstract: We give an attribution method for neural combinatorial-optimisation (CO) policies that (i) decomposes a decision by constraint families via LP-relaxation duals, (ii) certifies counterfactuals through a combinatorial feasibility model (implemented as a CSP feasibility-decision model), and (iii) bounds the size of a PAC-sufficient explanation with a Bonferroni-corrected Hoeffding sufficient-subset test along a greedy ordering. Across three CO problems and three seeds, our LP-anchored $\Lambda$-attribution matches the CF-derived signal at 96.5% on C
The increasing complexity and opacity of AI decision-making necessitates robust explanation methods, particularly as AI advances into critical applications like combinatorial optimization.
This development offers a significant step towards trustworthy and interpretable AI, crucial for adoption in sensitive and high-stakes domains where verifiable decision-making is paramount.
We now have a method to attribute decisions in neural combinatorial optimization policies with certified feasibility and bounded explanation sizes, enhancing trust and auditability.
- · AI explainability researchers
- · Industries deploying high-stakes AI (e.g., logistics, finance)
- · Regulatory bodies focused on AI ethics and transparency
- · Black-box AI development approaches
Increased adoption of neural combinatorial optimization in real-world, high-impact scenarios due to enhanced trust.
Development of regulatory frameworks that incorporate explainability and certified feasibility as core requirements for AI systems.
A shift in AI research priorities towards integrated explainability, rather than treating it as a post-hoc add-on.
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