arXiv:2606.15273v1 Announce Type: new Abstract: Shapley value-based feature attribution methods face challenges in scenarios involving complex feature interactions and causal relationships, even when a causal structure is provided. Existing methods typically adopt a node-centric view, attributing importance solely to individual features. Consequently, they often fail to simultaneously capture the externality and exogenous influence of features, leading to unreasonable interpretations. To overcome these limitations, we propose a novel feature attribution method called DAG-SHAP, which is based o
The proliferation of complex AI models and the increasing demand for explainability in critical applications drive the need for improved feature attribution methods.
This research offers a refined approach to understanding AI model decisions, which is crucial for trust, auditability, and the responsible deployment of AI agents.
The ability to more accurately attribute features in directed acyclic graphs could lead to more robust, interpretable, and less biased AI systems, especially in scenarios with complex causal relationships.
- · AI developers
- · AI ethics and auditing firms
- · Sectors requiring high AI explainability (e.g., finance, healthcare)
- · Developers of less transparent black-box AI systems
- · Organizations relying on simplistic feature attribution for compliance
Improved interpretability of complex AI models becomes more accessible, bridging the gap between performance and understanding.
Enhanced interpretability leads to more reliable and trustworthy AI deployments, accelerating adoption in sensitive domains.
Increased transparency and trust foster greater public acceptance of advanced AI systems, potentially influencing regulatory frameworks globally.
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