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Neuro-Symbolic Fraud Model Argues for Built-In Explainability at Inference Time
A reproducible benchmark claims a neuro-symbolic fraud model can generate deterministic explanations inside the forward pass far faster than SHAP KernelExplainer, highlighting a push toward real-time explainable AI.
Key Takeaways
- The benchmark reports 0.9 ms explanation latency for a neuro-symbolic model versus about 30 ms for SHAP KernelExplainer.
- Fraud recall was reported as identical in the experiment, with a small AUC drop.
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DT Editorial Team··via towardsdatascience.com