explainable-ai

This paper proposes using generative inpainting to create photorealistic perturbations for LIME, improving the quality of explanations by avoiding out-of-distribution artifacts common in traditional occlusion methods.

CoWeaver is a bidirectional, learnable, and explainable matching engine designed to form strong collaborations between humans and LLM-based agents in scientific networks, using capability gap filling, two-stage ranking, and uncertainty-aware exploration.

This paper proposes a method to extract an executable Prolog program from a deep reinforcement learning policy, providing theoretical guarantees on return and fidelity, enabling interpretability and manual editing.

This paper introduces MAR-12, a framework using Vision-Language Models and multi-angle reasoning to detect and explain harmful humor in memes, achieving state-of-the-art accuracy on PrideMM and Memotion datasets.

ReasonGate is an explainable security gate for LLM applications that blocks prompt injection attacks and provides an auditable reason for each decision.

This paper introduces Inductive Logic Programming to extract symbolic representations of RL policies and proposes novel explainability metrics (activation rate, feature coverage, syntactic and semantic distance) for objective evaluation in single- and multi-agent settings.

This paper presents an explainable AI approach for detecting anomalies in banking transactions from an internal audit perspective, addressing interpretability and trust in financial security systems.

A systematic survey of Federated Explainable Artificial Intelligence (FedXAI), covering roles, architectures, evaluation practices, and open challenges. It presents a multi-axis taxonomy and discusses model-agnostic to interpretable-by-design approaches, highlighting gaps in standardization and privacy-aware evaluation.

ConceptSMILE is a perturbation-based auditing framework for evaluating the reliability of concept-based explainable AI, tested on retinal fundus images.

This paper proposes a complementarity-theoretic interpretation for integrating knowledge graphs and explainable AI to support defensible decisions in urban mining pre-demolition assessment. It defines four KG-XAI integration modes (Lifting, Constraining, Typing, and Revising) and illustrates them with a fire-door example.

This paper introduces ExplAIner, a declarative query language for explaining classification models, addressing limitations of the FOIL language by supporting abductive, contrastive, and optimality-based explanations with tractable evaluation over Boolean circuits.

This paper introduces Agentic SABRE, an uncertainty-aware neuro-symbolic multi-agent framework for adaptive ransomware detection that fuses semantic and behavioral evidence with Monte Carlo Dropout and interpretable risk-uncertainty triage, demonstrating improved robustness and explainability.

This paper presents ShishuRaksha AI, a training-free multimodal framework for screening abuse-related trauma in Bangladeshi children, evaluated on a noise-aware synthetic dataset, achieving an AUC of 0.874.

This paper proposes an explainable entity-centric reinforcement learning framework for adaptive traffic signal control, using a dual-stage attention network with multi-head cross-attention and self-attention to provide interpretable affinity matrices, while integrating deterministic action masking in PPO for safety compliance.

This paper introduces a human-in-the-loop framework for personalized algorithmic recourse that iteratively approximates a user's causal model through Bayesian inference, improving the plausibility and cost-effectiveness of recommendations.

This paper introduces PACE, a modular neuro-symbolic framework that combines a neural predictive model with symbolic reasoning to generate counterfactual explanations that respect domain-specific feasibility constraints. A case study on the Adult Income dataset demonstrates that incorporating symbolic rules yields more plausible and actionable explanations.

This chapter reviews current understanding of Large Language Models, discussing their Transformer architecture, emergent capabilities resembling human cognition, and debates about whether LLMs genuinely understand or merely simulate understanding.

Introduces Neuro-Bayesian-Symbolic Residual Attention Shallow Network (NBS-RASN), a hybrid neural architecture for explainable cybersecurity risk assessment in open-source ecosystems, using 80 interpretable neurons across 12 layers with hard constraints for interpretability.

Introduces Partition-Guided Distance Saliency (PGDS), a novel XAI framework for many-objective optimization that uses geometric intuition to explain how decision variables influence objective space proximity, validated on 10-objective benchmarks and a physics-informed engineering problem.

This paper evaluates the zero-shot capability of multimodal large language models (MLLMs) for localized concept naming in images, proposing a reproducible evaluation protocol that achieves 62-88% object-level accuracy without training.