Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Explainable Artificial Intelligence (XAI)
Abstract
The increasing prevalence of complex AI models, particularly deep neural networks, in critical decision-making domains has raised significant concerns regarding transparency and trust. These "black-box" models often operate as opaque systems, making it challenging to understand *why* a particular decision was made. This paper proposes a novel approach to explainable AI (XAI) based on graph representation. We argue that representing the decision-making process as a graph—where nodes represent features and edges represent relationships—provides an intuitive and effective method for visualizing and understanding the model's reasoning. The framework presented offers a structured approach to building XAI systems, facilitating trust and accountability in AI-driven decisions. We outline the core components of this graph-based XAI system and discuss its potential applications across various domains.
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Agile methods continue to gain popularity. In particular, the Scrum method appears to be on the verge of becoming a de-facto standard in the industry, leading the so called Agile movement. While there are success stories and recommendations, there is little scientifically valid evidence of the challenges in the adoptio...
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The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
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This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
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The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
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