Explainable AI Explained
Software Engineering Institute
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 SEI Podcast, Violet Turri and Rachel Dzombak discusses explainable AI, which encompasses all the techniques that make the decision-making processes of AI systems understandable to humans.
About the Speaker
Violet Turri is an assistant software developer in the SEI AI Division where she works on multiple machine-learning engineering projects with an emphasis on explainability, test and evaluation strategies, and computer vision. Turri holds a bachelor’s degree in computer science from Cornell University and has a research background in human-computer …Read more