EXPLAINABLE ARTIFICIAL INTELLIGENCE IN HIGH-DIMENSIONAL DATA ANALYSIS: THEORETICAL FOUNDATIONS, INTERPRETABILITY CHALLENGES, AND METHODOLOGICAL PERSPECTIVES
DOI:
https://doi.org/10.5281/Keywords:
Explainable Artificial Intelligence; high-dimensional data; model interpretability; machine learning; feature importance; model transparency; explainability; artificial intelligence.Abstract
This article examines the theoretical foundations and methodological challenges of Explainable Artificial Intelligence (XAI) in high-dimensional data analysis. The study focuses on the growing interpretability problem associated with complex artificial intelligence models operating on data characterized by a large number of variables and intricate relationships. Theoretical approaches to explainability, model transparency, feature attribution, and post-hoc interpretation are systematically analyzed. Particular attention is given to the tension between predictive performance and interpretability, as well as to the limitations of conventional explanation techniques in high-dimensional settings. The analysis demonstrates that effective XAI requires not only accurate predictions but also explanations that are stable, meaningful, context-sensitive, and understandable to users. A conceptual methodological perspective for developing more interpretable AI systems is proposed.
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