Explainable AI (XAI) is an emerging field in machine learning that aims to address how black box decisions of AI systems are made (‘Black box’ refers to the difficulty in interpreting the decision making process of an AI). XAI term tends to refer to the initiatives and efforts made to tackle AI transparency and trust concerns. In layman’s terms, XAI tries to help understand the key parameters in the algorithm which have contributed to the decision making process. Model accuracy and explainability are the bedrock for XAI.
ML interpretability is a subset of XAI. ML interpretability is a core concept of XAI and helps humans build trust in AI systems. Interpretability in models help us evaluate their decisions and obtain information that the end result alone cannot confer. The trust in the system is generated by concentrating on three aspects :
As Machine Learning and Artificial Intelligence gain traction in the current business milieu, it is vital that data leaders foray into more robust and understandable data models to help organizations make data driven decisions which focuses both on accuracy and explainability.
To get an in-depth understanding of XAI and the most commonly used XAI algorithms, kindly watch the session by Anirban Nandi.