Explainable Artificial Intelligence: A Study of Current State-of-the-Art Techniques for Making ML Models Interpretable and Transparent
Ayush Thakur, Rashmi Vashisth, Sudhanshu Tripathi · 2023
Artificial intelligence and Machine learning are becoming more prevalent in a variety of industries, necessitating an increased the demand for systems that are capable of explaining their decision-making processes to human users. The idea behind of “Explainable AI” is to create AI systems that can offer clear and reasoned arguments for their activities they perform. This research looks on new approaches for increasing transparency and interpretability in machine learning models. Our focus is on the wide range of different XAI approaches that have been put forth, with a new focus on how broadly applicable they are. Apart from that, we will look into the significant challenges that must be overcome to support the development of ML models that are both transparent and accessible.