what is explainable ai

Explainable AI (XAI), also known as Interpretable AI, or Explainable Machine Learning (XML), is a set of processes and methods that allow human users to understand and trust the results and output created by AI/ML algorithms. The explanations accompanying AI/ML output may target users, operators, or developers and are intended to address concerns and challenges ranging from user adoption to governance and systems development. The goal of explainable AI is to answer stakeholder questions about the decision-making processes of AI systems. Developers and machine learning practitioners can use explanations to ensure that ML model and AI system project requirements are met during building, debugging, and testing. Explanations can also be used to help non-technical audiences, such as end-users, gain a better understanding of the AI system. Despite the growing interest in XAI research and the demand for explainability across disparate domains, XAI still suffers from a number of limitations.