Toward Faithful Neural Network Intrinsic Interpretation With Shapley Additive Self-Attribution
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces the Shapley Additive Self-Attributing Neural Network (SASANet), a novel framework for self-interpreting neural networks. SASANet provides genuine interpretability and enhanced model expressiveness by integrating Shapley value attribution.
Area Of Science
- Artificial Intelligence
- Machine Learning
- Explainable AI (XAI)
Background
- Current self-interpreting neural networks often lack theoretical grounding for interpretability and limit model expressiveness.
- Existing methods struggle to provide genuine, theoretically sound explanations for model predictions.
Purpose Of The Study
- To propose a generic additive self-attribution (ASA) framework to unify existing approaches.
- To introduce a novel Shapley Additive Self-Attributing Neural Network (SASANet) that incorporates Shapley value attribution for enhanced interpretability.
- To address the limitations of current self-interpreting models by improving both interpretability and performance.
Main Methods
- Developed a novel Shapley Additive Self-Attributing Neural Network (SASANet).
- Designed an intermediate sequential schema utilizing marginal contributions (MCs) and an internal distillation procedure.
- Theoretically proved that the intermediate self-attribution values converge to the output's Shapley values.
Main Results
- SASANet achieves high interpretability and outperforms existing self-attributing models in performance.
- SASANet's performance is comparable to commonly used closed-box models.
- The self-attribution method in SASANet offers more accurate and efficient interpretations than post hoc methods.
Conclusions
- SASANet is the first self-interpreting neural network structure to achieve model-wise Shapley attribution.
- The proposed framework enhances both the interpretability and performance of neural networks.
- SASANet offers a theoretically sound and practically effective approach to explainable AI.
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