Abstract:

Thulasiram Prasad Pasam

Description:

Abstract– This research reviews explanation and interpretation for Explainable Artificial Intelligence (XAI) methods in order to boost complex machine learning model interpretability. The study shows the influence and belief of XAI in users that trust an Artificial Intelligence system and investigates ethical concerns, particularly fairness and biasedness of all the non-transparent models. It discusses the shortfalls related to XAI techniques, putting crucial emphasis on extended scope, enhancement and scalability potential. A number of outstanding issues-especially in need of further work can involve standardization, user-centered design and interdisciplinary in strategies for improving the practical utility of XAI.