EXPLAINABLE ARTIFICIAL INTELLIGENCE (XAI) MODELS FOR HIGH-STAKES DECISION-MAKING SYSTEMS (ОБРАЗОВАНИЕ НАУКА И ИННОВАЦИОННЫЕ ИДЕИ В МИРЕ, 2025)
As artificial intelligence (AI) continues to permeate high-stakes domains, such as healthcare and finance, the demand for Explainable Artificial Intelligence (XAI) has become increasingly urgent. The necessity for transparency in AI-driven decisionmaking systems arises not only from ethical considerations but also from the inherent complexities associated with machine learning models, which can often render their outputs opaque to users. It is critical to recognize that while AI models may demonstrate accuracy on averaged data, they can lack reliability when applied to specific individuals, necessitating robust frameworks for personalized uncertainty quantification ((Banerji et al., 2025)). XAI aims to bridge this gap by providing interpretable models or post hoc explanations that enhance human understanding and trust in AI systems ((Finzel et al., 2025)). Furthermore, as organizations navigate ethical and managerial implications, augmented leadership is essential for integrating AI insights while fostering transparency and combating biases ((Erhan et al., 2025), (Thulasiram et al., 2025)). Thus, XAI models serve as a vital component of responsible decision-making in high-stakes environments.