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Neuro-Symbolic Fraud Model Argues for Built-In Explainability at Inference Time
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.
- The article argues explainability should be integrated into model architecture rather than added post hoc.
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DT Editorial Team··via towardsdatascience.com