explainable-ai

This paper introduces a parallelization strategy and adaptive steering mechanism for the Baymex algorithm to efficiently learn discretized Bayesian network classifiers for clinical data, achieving speedups over 54x on a 16-core CPU and comparable or better predictive performance than traditional models while maintaining explainability.

This paper compares several post-hoc explainability methods applied to an InceptionTime model for EEG-based depression detection, finding partial convergence among methods while highlighting methodological variability and limitations.

N2I-RAG is a framework that combines adaptive retrieval, LLM agents, and validation to compute legal indicators from normative texts, with a focus on transparency and traceability. It outperforms baselines on a French marine environmental law corpus.

This paper presents MEMOR-E, a mobile quadruped robot with a tablet interface that uses fine-tuned and in-context learning with LLMs to provide personalized, stage-aware cognitive assistance for Alzheimer's patients, including medication reminders and memory interactions, with explainable AI for caregiver oversight.

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 deterministic, rule-based sleep staging method that explicitly implements the American Academy of Sleep Medicine (AASM) scoring rules, providing epoch-level natural language explanations. It achieves 60.5% epoch-level agreement with a majority-vote consensus on 50 polysomnography recordings, offering transparency as a complement to opaque deep learning models.

This paper presents a framework for prototype-based explanations that integrates feature importance at local and global levels, using 'alike parts' to highlight relevant feature subsets and augmenting prototype selection with feature diversity, evaluated on tabular datasets.

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.

This paper proposes a model-agnostic probabilistic token attribution measure for LLMs using Bayes' rule to invert next-token log probabilities, capturing the model's internal representation of token sequences and improving interpretability through entropy analysis.

This paper presents an experimental study investigating whether conversational XAI assistants improve user performance in terms of prediction accuracy, model understanding, and error identification compared to Q&A-based assistance, with preliminary results showing no significant performance differences.

Introduces Peak-Detector, a framework that uses instruction-tuned large language models for robust, cross-modal, and explainable peak detection in physiological signals like ECG, PPG, BCG, and BSG. The method transforms time-series data into a condensed 'peak-representation' format and is optimized via supervised fine-tuning followed by reinforcement learning with a multi-objective reward.

A 16-year-old developer created sage-explainer, a Python package that approximates prediction sensitivity to features for black-box models like random forests and XGBoost, offering more stable results than centered finite differences.

Introduces a weight perturbation-based feature attribution method (XWP and XWPc) for fully connected neural networks, achieving competitive performance on standard baseline metrics.

This paper describes two models for vocabulary difficulty prediction: a black-box LLM fine-tuned with a soft-target loss achieving high accuracy, and an explainable model providing insights into difficulty factors. The models were part of the BEA 2026 Shared Task and achieve strong correlations.

The paper introduces AGOP-Weighted, a post-hoc attribution method that multiplies per-sample gradients by a training-distribution prior to suppress noise and highlight important pixels, and demonstrates significant improvements over existing methods on synthetic and photorealistic benchmarks.

This academic paper establishes connections between Consistency-Based Diagnosis and Actual Causality within the context of Explainable AI (XAI). It aims to integrate these two areas to improve explanations in AI and Explainable Data Management.

A research paper proposing a four-stage hybrid framework for solar and wind energy forecasting, utilizing a quantum-inspired variational kernel for residual correction and a generative AI layer for explainability.

This paper introduces the Explanation Fairness Taxonomy (EFT) to analyze disparities in how LLMs justify decisions across demographic groups, finding significant biases in explanation quality and tone despite balanced decisions.

This paper introduces PathBoost, a gradient tree boosting method for graph-level prediction that uses path-based features to compete with graph neural networks while offering better interpretability.

This paper evaluates explainability methods in safety-critical Automatic Target Recognition (ATR) systems, highlighting the limitations of post-hoc techniques like saliency and attention maps. It proposes a taxonomy and assessment framework to address issues such as spurious explanations and instability, advocating for more robust, causally grounded XAI approaches.