As the field of artificial intelligence (AI) has matured, increasingly complex opaque models have been developed and deployed to solve hard problems. Unlike many predecessor models, these models, by the nature of their architecture, are harder to understand and oversee. When such models fail or do not behave as expected or hoped, it can be hard for developers and end-users to pinpoint why or determine methods for addressing the problem. Explainable AI (XAI) meets the emerging demands of AI engineering by providing insight into the inner workings of these opaque models. In this podcast from the Carnegie Mellon University Software Engineering Institute (SEI), Violet Turri and Rachel Dzombak, both with the SEI's AI Division, discuss explainable AI, which encompasses all the techniques that make the decision-making processes of AI systems understandable to humans. 

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