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ANALYSIS OF DATA FRAGMENTATION IN ARTIFICIAL INTELLIGENCE MODELS TO IMPROVE DECISION-MAKING PROCESSES IN SOCIALLY SIGNIFICANT AREAS
Abstract
Artificial intelligence (AI) is rapidly penetrating all forms of digitised systems and services, including organisational, social, economic, and governmental, revolutionising the way information is processed, and decisions are made. The integration of AI in socially relevant areas is increasingly essential due to the growing use of digital services and the extensive, fragmented information spread across platforms. The lack of a unified approach to processing this data creates challenges in accessibility, reliability assessment, and decision-making efficiency. This paper examines the need for AI-driven automation in administrative services and policy formulation. This study follows a systematic approach, including literature review and comparative assessment of AI applications. It explores the potential role of language models, with focus on OpenEuroLLM and OpenAI, alongside other leading solutions. A conceptual AI pipeline is proposed as a framework for integrating heterogenous data originating from fragmented public information sources. The findings indicate that AI can significantly enhance the efficiency and accessibility of public services by facilitating information processing and synthesis. AI technologies, while powerful, face numerous technical issues and practical limitations. As well as challenges such as ethics, data privacy, and human oversight remain critical. The study highlights the need for a balanced approach that integrates technological advancements with social responsibility and regulatory compliance.
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References7
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