Explainable AI: what is it and who cares?

In this Q&A on Explainable AI, Andrea Brennen speaks with In-Q-Tel's Peter Bronez about descriptive vs. prescriptive models, "white box" vs. "black box" explanation techniques, and why some models are easier to explain than others. Peter also discusses the reproducibility crisis in Psychology and why good experiment design is so important. Peter is a VP on the technical staff at IQT. Could you tell me about your experience with machine learning and AI? PETER: As an undergraduate, I studied econometrics and operations research, so my exposure to machine learning was in the context of designing models of the world that you could test mathematically -- basically, doing hypothesis testing using statistics. Afterwards, I worked at the Department of Defense and used a lot of the same techniques. From there, I went to the private sector and [worked on] social media and data mining in marketing applications, trying to create mathematical models to categorize people, activities, and messages in order to understand them better.