FAME: Formal Abstract Minimal Explanation for Neural Networks
arXiv:2603.10661v1 Announce Type: new Abstract: We propose FAME (Formal Abstract Minimal Explanations), a new class of abductive explanations grounded in abstract interpretation. FAME is the first method to scale to large neural networks while reducing explanation size. Our main contribution is the design of dedicated perturbation domains that eliminate the need for traversal order. FAME progressively shrinks these domains and leverages LiRPA-based bounds to discard irrelevant features, ultimately converging to a formal abstract minimal explanation. To assess explanation quality, we introduce a procedure that measures the worst-case distance between an abstract minimal explanation and a true minimal explanation. This procedure combines adversarial attacks with an optional VERIX+ refinement step. We benchmark FAME against VERIX+ and demonstrate consistent gains in both explanation size and runtime on medium- to large-scale neural networks.
arXiv:2603.10661v1 Announce Type: new Abstract: We propose FAME (Formal Abstract Minimal Explanations), a new class of abductive explanations grounded in abstract interpretation. FAME is the first method to scale to large neural networks while reducing explanation size. Our main contribution is the design of dedicated perturbation domains that eliminate the need for traversal order. FAME progressively shrinks these domains and leverages LiRPA-based bounds to discard irrelevant features, ultimately converging to a formal abstract minimal explanation. To assess explanation quality, we introduce a procedure that measures the worst-case distance between an abstract minimal explanation and a true minimal explanation. This procedure combines adversarial attacks with an optional VERIX+ refinement step. We benchmark FAME against VERIX+ and demonstrate consistent gains in both explanation size and runtime on medium- to large-scale neural networks.
Executive Summary
This article proposes FAME (Formal Abstract Minimal Explanations), a novel method for providing abductive explanations in neural networks. FAME leverages abstract interpretation and perturbation domains to generate explanations that are both minimal and formal. The authors demonstrate the scalability and effectiveness of FAME on medium- to large-scale neural networks, achieving consistent gains in explanation size and runtime compared to VERIX+. The article introduces a procedure for measuring explanation quality, combining adversarial attacks and an optional VERIX+ refinement step. This methodological contribution has significant implications for the development of explainable AI and the evaluation of neural network models.
Key Points
- ▸ FAME is a novel method for providing abductive explanations in neural networks
- ▸ FAME leverages abstract interpretation and perturbation domains to generate explanations
- ▸ The authors demonstrate the scalability and effectiveness of FAME on medium- to large-scale neural networks
Merits
Strength in Methodological Contribution
The article introduces a novel method for providing abductive explanations in neural networks, leveraging abstract interpretation and perturbation domains to generate explanations that are both minimal and formal.
Scalability and Effectiveness
The authors demonstrate the scalability and effectiveness of FAME on medium- to large-scale neural networks, achieving consistent gains in explanation size and runtime compared to VERIX+.
Demerits
Limited Comparison to Existing Methods
While the article demonstrates the effectiveness of FAME, it would be beneficial to include a more comprehensive comparison to existing methods for providing abductive explanations in neural networks.
Potential for Further Refinement
The authors' procedure for measuring explanation quality is a significant contribution, but the article could benefit from further refinement and validation of this method.
Expert Commentary
The article represents a significant advancement in the field of explainable AI, leveraging novel methodological contributions to provide more effective and efficient abductive explanations in neural networks. The authors' demonstration of FAME's scalability and effectiveness on medium- to large-scale neural networks is a notable achievement, with significant implications for the development of explainable AI and the evaluation of neural network models. However, the article could benefit from further refinement and validation of the authors' procedure for measuring explanation quality, as well as a more comprehensive comparison to existing methods for providing abductive explanations in neural networks.
Recommendations
- ✓ Future research should focus on further refining and validating the authors' procedure for measuring explanation quality.
- ✓ A more comprehensive comparison to existing methods for providing abductive explanations in neural networks would provide a more complete understanding of FAME's strengths and limitations.