arXiv Machine Learning September 7, 2026 By Athina Georgara, Adarsh Valoor, Sarvapali D. Ramchurn

ProToMEx: Rapid, Interpretable Explanations via Structured Representations

Read the original on arXiv Machine Learning →

ProToMEx is a new explainability framework that uses Probabilistic Topic Models to learn latent topics representing high‑level reasons behind a classifier’s decisions, moving beyond simple feature attribution. It provides both global and local explanations, revealing multiple co‑existing reasons for individual predictions. Empirical results show that ProToMEx achieves comparable fidelity to SHAP and LIME while being 30–40× faster on standard tabular and synthetic datasets.

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