Enhancing Transparency and Trust in Machine Learning Models through Explainable AI (XAI)
Year: 2025
Type: article
Abstract: Explainable Artificial Intelligence (XAI) aims to make the behaviour and decisions of machine learningmodels understandable to humans. This paper reviews current XAI methods, implements severaltechniques on a relevant case study, and evaluates their effectiveness in terms of interpretability andperformance trade-offs. The study demonstrates that XAI techniques such as LIME and SHAP can providemeaningful insights into complex models without substantially compromising their performance.
Author: Vishal Kumar Singh, Anuradha Sharma, Dr.Kumar Amrendra
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6949889094
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