ENSURING THE RELIABILITY OF SCIENTIFIC RESULTS BASED ON EXPLAINABLE AI (XAI) (Universal International Scientific Journal, 2026)
This paper investigates the role of Explainable AI (XAI) technologies in analyzingscientific results and enhancing their reliability in natural and exact sciences. The study employsmodern XAI methods such as LIME and SHAP to assess model transparency, error rates, andalignment with expert evaluations. Results indicate that XAI methods improve model reliability by23–30%, reduce incorrect decisions, and strengthen trust in scientific research. The findingsdemonstrate the practical applicability of XAI approaches as an effective tool in scientific studies. Thedevelopment of automated scientific platforms based on XAI is considered a relevant task for futureresearch and practice.
Keywords: Explainable AI; XAI; LIME