Although explainability is critical for trustworthy AI, Group Recommender Systems (GRS) lack dedicated, flexible software tools. To address this, we present GREX, a novel, open-source Python library designed to facilitate the development and evaluation of explanations in group settings. GREX is built on a modular and extensible architecture, providing implementations of three distinct explanation paradigms: counterfactual (Sliding-Window-Weighted), rule-based (EXPGRS), and local model-agnostic (LORE4GROUPS). We conducted a comprehensive description of GREX stages, overall including data preparation, training, recommendation, and explanation. GREX is focused on empowering researchers and practitioners to systematically develop and compare Explainable AI (XAI) methods, fostering progress in transparent and user-centric GRS.
The lack of proper formalization in non-symbolic explainable artificial intelligence (XAI) has resulted in a growing number of provable flaws in widely adopted methods of explainability. This paper reviews some of the most visible misconceptions of non-symbolic XAI, and shows how logic-based XAI has been applied to uncover and correct those misconceptions. The paper also summarizes ongoing research to develop a sound, scalable and human-understandable framework for rigorously defined XAI.
The growing deployment of large language models (LLMs) in policy-sensitive and distributed environments raises concerns about hallucinations, semantic opacity, and lack of traceability, especially in regulated domains such as healthcare and finance. Retrieval-augmented generation (RAG) helps address these issues by grounding outputs in external sources, while data spaces (DSs) offer a governance-driven infrastructure for federated and sovereign data exchange. However, their integration remains underdeveloped, limiting the design of AI systems that are both explainable and compliant with regulatory demands. This paper analyzes three RAG–DSs integration models from the literature and introduces two new architectures, Guided RAG and Federated RAG, that directly address their structural limitations by combining coordination, data sovereignty, and trust in generative systems. Guided RAG and Federated RAG are formally specified using Business Process Model and Notation (BPMN), and all five models, together with a non-retrieval baseline, are analyzed across operational dimensions including transparency, traceability, and control delegation. This work establishes an architectural foundation for trustworthy generative AI operating under distributed governance constraints, with explicit guarantees of data sovereignty. The proposed framework supports systematic evaluation in federated settings and serves as a foundation for methodological refinement and regulatory alignment.
This research examines the utilization of Inverse Reinforcement Learning and Deep Q-Networks in traffic signal management to enhance urban mobility through the automation of reward function design. The study illustrates an 81.35
The increasing reliance on artificial intelligence (AI) in education has highlighted the necessity for transparent and trustworthy recommender systems. This paper presents a framework that combines collaborative filtering, specifically Singular Value Decomposition (SVD), with post-hoc, model-agnostic explanation techniques to generate interpretable recommendations in e-learning environments. To achieve this, we implement both crisp and fuzzy association rule mining approaches for explaining recommendations based on learners’ historical behaviors. The fuzzy model leverages membership degrees to account for interaction intensity, while the crisp model uses binarized user–item transactions. We generate explanations by mapping recommended items to association rules, and explanation quality is assessed using the Average Fidelity metric. Experimental evaluations on a real-world MOOC dataset demonstrate the effectiveness of the framework in delivering recommendations with interpretable justifications. The results validate the feasibility of integrating fuzzy-based explainability into recommendation pipelines, as the proposed approach enhances the crisp-based explanation approach, taking into account fidelity-based measures.
