A fraud model's decision can be fully explainable yet still wrong because the underlying data pipeline fails to capture document provenance, valuation timing, and feature timestamp policies, according…
A developer built an Explainable Causal Reinforcement Learning (XC-RL) framework for wildfire evacuation logistics, incorporating a Structural Causal Model to capture causal relationships between fire…
A multi-scale stacking ensemble for credit risk scoring reached test ROC-AUC 0.9539 and PR-AUC 0.9137 on a 32,581-application dataset, beating the best single model by Delta-AUC 0.0143, but an audit o…
A practical guide argues that churn prediction models fail unless paired with prescriptive actions, urging data teams to segment at-risk users by churn driver using SHAP or LIME, map drivers to specif…
A study using BERT, a transformer-based AI model, achieved 97.05% accuracy in multilabel classification of 14,590 Mpox research articles into topics such as outbreaks, vaccination, and epidemiology, a…
A developer demonstrates how to train an LSTM on daily temperature data and apply three explainability methods—permutation importance, SHAP, and Integrated Gradients—to interpret the model's predictio…
SHAP (SHapley Additive exPlanations) is the most effective tool for debugging machine learning models, particularly for detecting data leakage bugs where a feature contains target information unavaila…
A developer built predictive maintenance systems for aircraft using machine learning, converting operational data into maintenance support for inspection planning and fault detection. The workflow inv…
A new set of lecture notes from arXiv:2604.07520v2 provides a framework for performing global statistical fits in high-energy physics using Machine Learning surrogates, including Boosted Decision Tree…
Explainable AI (XAI) is essential for deploying machine learning in high-stakes domains like healthcare, finance, and autonomous vehicles, where opaque models can lead to liability, non-compliance, or…
SHARC, a new framework for SHapley Additive exPlanations for Regulatory Capital, makes Gaussian Process Regression models auditable by decomposing Stressed Value-at-Risk into baseline, mean-driven, an…
Researchers developed an AI-driven MRI protocol that cuts scan time from 27 to 14 minutes using an explainable AI framework with XGBoost, SHAP, and Recursive Feature Elimination, reducing features to …
Researchers developed an AI pipeline that digitizes paper ECGs via smartphone photos, achieving 95.51% accuracy in detecting myocardial infarction. The system runs on CPUs in under 30 seconds, enablin…
Researchers developed a reduced NEXI protocol for quantifying human gray matter microstructure on the Connectome 2.0 scanner, using an Explainable AI framework to cut scan time from 27 to 14 minutes. …
Researchers proposed an uncertainty-aware reinforcement learning framework for algorithmic trading that integrates distributional, epistemic, and aleatoric uncertainty estimations using SHAP-weighted …
Researchers used interpretable machine learning to predict Parkinson's disease motor severity from QSM and fMRI features, achieving 45.4% variance explained and 75% of predictions within 5 points of c…
A tutorial demonstrates building an autonomous AI co-scientist for EGFR C797S inhibitor discovery using ChEMBL, RDKit, SHAP, and BRICS. The system mines IC50 data, trains a scaffold-split Random Fores…
Researchers at the University of Benghazi are conducting a systematic review comparing interpretable statistical models with explainable AI approaches for predicting breast cancer recurrence. The revi…
Two independent studies published June 27, 2026, developed machine learning classifiers to distinguish life-threatening EBV-associated hemophagocytic lymphohistiocytosis (EBV-HLH) from self-limited Ep…
Researchers Antoine Pesenti and Aidan O'Sullivan combined deep neural networks with explainable AI techniques to analyze electricity price formation across 39 European bidding zones, identifying renew…