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Explainable AI (XAI), often overlapping with interpretable AI, or explainable machine learning (XML), is a field of research within artificial intelligence (AI) that explores methods that provide humans with the ability of intellectual oversight over AI algorithms.
The field of Explainable AI seeks to provide better explanations from existing algorithms, and algorithms that are more easily explainable, but it is a young and active field. [ 18 ] [ 19 ] Others argue that the difficulties with explainability are due to its overly narrow focus on technical solutions rather than connecting the issue to the ...
Explainable AI (XAI) methods, such as visualizations of decision pathways or feature importance metrics, make these explanations accessible and comprehensible, allowing users to make informed decisions about whether to trust the algorithm.
That could mean being able to ask an AI agent an ambiguous question and have the model able to reason and plan out steps to solving an ambitious problem, Pandey said. A lot of technology, he said ...
Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems.It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. [1]
He contends with their concerns: “Humanity faces risks and we have to manage them,” he said, “but that doesn't mean we completely forgo the benefits of especially powerful technologies like ...