Explainable AI (XAI)
Explainable AI (XAI) is a branch of artificial intelligence (AI) research that focuses on making AI systems more transparent and interpretable. XAI techniques are designed to provide explanations for the decisions made by AI systems, allowing users to better understand and trust the decisions made by the
Within artificial intelligence (AI), explainable AI (XAI), generally overlapping with interpretable AI or explainable machine learning (XML), is a field of research that explores methods that provide humans with the ability of intellectual oversight over AI algorithms. The main focus is on the reasoning behind the decisions or predictions made by the AI algorithms, to make them more understandable and transparent. This addresses users' requirement to assess safety and scrutinize the automated decision making in applications. XAI counters the "black box" tendency of machine learning, where even the AI's designers cannot explain why it arrived at a specific decision.