(untitled) (Universal International Scientific Journal, 2026)
This paper investigates the role of Explainable AI (XAI) technologies in analyzing scientific results and enhancing their reliability in natural and exact sciences. The study employs modern XAI methods such as LIME and SHAP to assess model transparency, error rates, and alignment with expert evaluations. Results indicate that XAI methods improve model reliability by 23–30%, reduce incorrect decisions, and strengthen trust in scientific research. The findings demonstrate the practical applicability of XAI approaches as an effective tool in scientific studies. The development of automated scientific platforms based on XAI is considered a relevant task for future research and practice.