Explainability of Complex AI Models with Correlation Impact Ratio
arXiv:2601.06701v2 Announce Type: replace-cross Abstract: Complex AI systems make better predictions but often lack transparency, limiting trustworthiness, interpretability, and safe deployment. Common post hoc AI explainers, such as LIME, SHAP…
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ImpactNotable 31/100
Why it mattersRule-based estimate: event keywords (+4); trust 6/10.
RegionsGlobal
Published4 h ago (Fri, 02 Oct 2026 04:00:00 GMT)
RetrievedFri, 02 Oct 2026 07:00:28 GMT via rss
ClassifiedFri, 02 Oct 2026 07:00:40 GMT by heuristic
AuthorPoushali Sengupta, Rabindra Khadka, Sabita Maharjan, Frank Eliassen, Yan Zhang, Shashi Raj Pandey, Pedro G. Lind, Anis…