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The paper proposes EPA-CarbonNet, a six-layer transformer architecture for carbon credit price prediction that integrates market data and policy text, but tests on S&P carbon index data show mixed results with a random walk outperforming on some metrics while directional accuracy is promising.
This paper introduces contrastive explanations for Quantitative Bipolar Argumentation Frameworks, explaining differences between two topic arguments to enhance AI explainability, with applications in healthcare and bias identification.
A biomedical-to-CS student built an explainable bone-lesion screener for X-rays using DenseNet-121, deployed on Cloudflare Workers with a £5 monthly cost, featuring heatmapping for explainability and out-of-distribution detection.
MIT and Motional researchers developed the Concept-Wrapper Network (CW-Net), a method that explains self-driving car decisions using understandable concepts, helping humans predict mistakes and improve safety. The approach, tested in road and simulation studies, enhances situational awareness for drivers and passengers.
This paper proposes a novel framework using Weight of Evidence to evaluate feature importance methods in explainable AI, enhancing their alignment with prior knowledge and stability assessment.
This paper examines the progression from inherent explainability in artificial intelligence to the development and application of explainable methods for large language models.
This review formalizes Explainable AI (XAI) methods in computational pathology by introducing definitions, a taxonomy, and task-driven recommendations to address clinical adoption challenges.
FRAC-MAS is an agentic AI system for automated, explainable, and safe bone fracture detection, integrating vision models with multi-agent workflows to enhance diagnosis and clinical reporting.
This paper proposes attribute-based activation steering to tailor LLM explanations to specific groups, achieving better specificity and factuality compared to prompting and state-of-the-art baselines.
The paper introduces ChemOntoRule, a proof-of-concept neuro-symbolic system that uses a task-centric ontology and deterministic rules to solve school-level chemistry problems, achieving high accuracy in a controlled evaluation.
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 feasibility study compares a standalone LLM with a pre-specified agentic pipeline for explaining ICU mortality predictions, finding that the agentic approach improves guideline grounding and patient-specific detail but requires attribution-based checks for safety.
This survey identifies three critical gaps in explainable AI for Arabic NLP—method, task, and linguistic—and proposes a taxonomy and research agenda for linguistically grounded explanations.
This paper proposes a training-time explainability framework for multilingual hate speech detection, aligning model reasoning with human rationales to improve classification performance and interpretability, evaluated on English and Hinglish datasets.
This paper introduces a methodological framework for auditing the robustness and fidelity of post-hoc explainable AI tools like SHAP and LIME, combining these metrics into a Trust Score. It applies the framework to a food security dataset in Madagascar, highlighting the necessity of auditing XAI outputs for trustworthy decision-making in sensitive domains.
This paper presents a systematic review of software frameworks for explainable AI in time series classification, comparing their features, evaluation practices, and limitations.
ConceptTS introduces an interpretable forecasting framework that uses large language models to propose human-readable concepts for multivariate time-series prediction, achieving competitive accuracy with transparency through concept bottlenecks.
This study develops a bankruptcy prediction framework using hybrid resampling, stacking ensembles, and explainable AI to enhance minority-class detection in imbalanced financial data. The results highlight GRU with SMOTE-ENN as the best performer and SHAP analysis for identifying key bankruptcy risk predictors.
BERTilda is an explainable framework that tracks topic lifecycles in longitudinal text streams by constructing temporal graphs with similarity and flow signals to detect splits, merges, and other transitions, achieving high agreement rates on annotated datasets.
CLS introduces a scalable framework for simultaneous causal network inference and forecasting in dynamical systems, achieving high-fidelity reconstruction and accurate predictions in benchmarks.