How Explainable Artificial Intelligence (XAI) Can Help Us Trust AI

Have you ever wondered how machine learning models work? Or what, exactly, goes on inside these models and whether we can trust them? Well, you're in luck, because I'm going to try to give you a very general overview of what XAI is and why we need it by answering a few common questions. After reading this, you should be able to understand the necessity of XAI and whether you need to start thinking about integrating it with your ML projects/products. Explainable AI (XAI) is a rather new field in machine learning (ML) in which researchers try to develop models that are able to explain the decision-making process behind ML models. XAI has many different research branches but, generally speaking, it either tries to explain the results of complex, black-box ML models or tries to incorporate interpretability into current ML architectures.