Methods, Techniques, and Application of Explainable Artificial Intelligence

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Author(s): Ankur Dumka (Women Institute of Technology, India), Vaibhav Chaudhari (Nutanix Technologies India Pvt. Ltd., India), Anil Kumar Bisht (MJP Rohilkhand University, India), Ruchira Rawat (Graphic Era University, India)and Arnav Pandey (St. Joseph's Academy, India)
Copyright: 2024
Pages: 18
Source title: Reshaping Environmental Science Through Machine Learning and IoT
Source Author(s)/Editor(s): Rajeev Kumar Gupta (Pandit Deendayal Energy University, India), Arti Jain (Jaypee Institute of Information Technology, India), John Wang (Montclair State University, USA)and Rajesh Kumar Pateriya (Maulana Azad National Institute of Technology, India)
DOI: 10.4018/979-8-3693-2351-9.ch017

Keywords: Engineering Science Reference / Environmental Science and Technologies / Knowledge Management and Library Science / Machine Learning

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Abstract

With advancement in machine learning, the use of machine learning has been increased, and explainable artificial intelligence (XAI) has emerged as an area of research and development for addressing the opacity and complexity of machine learning models. This chapter has proposed the overview of the current state of explainable artificial intelligence with highlighting its significance, disadvantages, and its potential applications in different fields. This chapter explores several explainable artificial techniques ranging from post-hoc methods like SHAP, LIME to decision tree and rule-based systems. This chapter also focusses on complexity and interpretability of a model.

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