shap

The study introduces SHAP-RTL, a rendering layer that corrects the visualization of SHAP and LIME explanations for right-to-left languages, addressing issues like token sequence and script shaping while preserving original attribution values.

The paper proposes a new evaluation method for machine learning explanations by converting explanations into predictors and testing their ability to reproduce model predictions. It demonstrates that the effectiveness of explanation methods like SHAP and PDP depends on the independence of features in the data.

This paper presents an explainable machine learning framework using SHAP analysis to predict and profile broadband adoption disparities at the census-tract level in the United States, identifying key factors like income and education for policy targeting.

The paper introduces a framework for integrating explainable AI into CRM systems for customer churn prediction in telecommunications, benchmarking classifiers and using SHAP and LIME for interpretable predictions to enhance retention strategies.

This article demonstrates how high validation accuracy can conceal proxy bias in AI models, using SHAP for explainability and a runtime governance wrapper to enforce fairness policies and prevent biased decisions in production.

This paper builds a multi-scale stacking ensemble for credit risk scoring and audits LLM-generated explanations, finding that ranking gains are real but small while the narrative explanations are often unfaithful, with SHAP and LIME agreeing on important features but not their order or sign.

This paper systematically evaluates 15 machine learning models, including the TabPFN foundation model, for post-wildfire debris-flow prediction using USGS basin-scale data, finding TabPFN achieves the best performance (threat score 0.637) and that synthetic data augmentation improves most models.

The paper investigates whether guideline-based categorical encodings of continuous predictors can replace continuous inputs in stroke outcome prediction models without sacrificing accuracy, finding comparable performance in most treatment cohorts.

An arXiv paper presents an interpretable machine-learning framework for predicting the splitting strength of asphalt concrete using SHAP analysis, comparing six models with TabPFN performing best.

This paper proposes an automated multilabel classification system for Mpox research articles using BERT, achieving 97% accuracy, and employs SHAP for explainability. The system aims to help researchers and healthcare workers quickly find relevant information.

This paper proposes SHAP-weighted cross-modal expert fusion (XGAF) for emotion and sentiment recognition, demonstrating that sum-abs SHAP aggregation achieves early-fusion-level performance on MELD and CMU-MOSEI datasets.

This article provides a systematic and comprehensive overview of AI explainability, covering its needs (debugging, compliance, safety), classic methods, and cutting-edge challenges, emphasizing that faithful explanations are more important than plausible ones.

This paper presents a multi-stage explainable framework that combines SHAP-based token attribution, theory-informed linguistic features, and LLaMA-3.1-70B-Instruct LLM reasoning to interpret transformer-based speech models for cognitive impairment detection, achieving strong clinical alignment and high usability scores.

This paper applies explainable AI techniques (SHAP, SSHAP) to deep neural network models to analyze drivers of electricity prices across 39 European bidding zones, finding that solar power and gas prices are key drivers despite solar's lower generation share.

This paper examines whether ML models can beat the random walk benchmark in forecasting USD/CAD exchange rates, finding that only linear regression statistically outperforms the naive model, with SHAP analysis showing short-term lags dominate predictions.

This paper proposes a framework for sentence-level interpretability of rubric-based scoring, comparing SHAP and LLM-generated rationales. It finds that fine-tuned pretrained language models outperform LLMs in prediction accuracy, and SHAP provides more faithful and transferable explanations.

This study develops an XGBoost classifier using SHAP explainability on eight clinical biomarkers from the ADNI dataset to achieve three-class Alzheimer's disease detection (normal cognition, MCI, AD), reaching a macro AUC of 0.982 and Cohen's kappa of 0.909 on the held-out test set. SHAP analysis identifies CDR Global as the dominant predictor for NC and MCI, while CDR-SB and MMSE together drive AD classification.

Proposes a verification-based algorithm to compute provable bounds on exact SHAP values for neural networks, scaling to much larger search spaces than prior exact methods.

This paper presents a machine learning framework using CatBoost and SHAP to predict obstructive coronary artery disease from CT calcium scoring scans, achieving high accuracy by combining calcium-omics and epicardial fat features.

This paper proves that no feature ranking can be simultaneously faithful, stable, and complete under collinearity, characterizing the full attribution design space and providing a formally verified impossibility theorem in explainable AI.