Dear all, Our next AI seminar is scheduled for Friday, January 24th. Talk details: AI Seminar: Explainable AI with Guarantees Speaker: Dr. Adnan Darwiche, Professor of Computer Science, UCLA Time: 2:00 PM Location: KEC 1001 and Zoom Zoom link: https://oregonstate.zoom.us/s/98357211915<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonstate.zoom.us%2Fs%2F98357211915&data=05%7C02%7Cai%40engr.oregonstate.edu%7C0714eb3b143f4f9c9c7f08dd3b28bcf0%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638731768330658894%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=GGq0cV1qtukMAN1%2Fth9r78awRCOAHsTAnRJEm%2FfKNcA%3D&reserved=0> Talk Abstract: I will discuss a theory of formal explainable AI that has emerged over the last few years, which is geared towards explaining the decisions of learned classifiers likes ones based on Bayesian Networks, random forests and some types of neural networks. The theory is based on compiling the input-output behavior of such classifiers into symbolic form and then using sophisticated machinery from symbolic logic to explain decisions. The theory employs newly-introduced, and profound, abstraction operators that distill the essence of an instance (i.e., classifier input) which causes the decision on that instance. Among the implications is an ability to identify minimal aspects of an instance that guarantee the decision, and minimal aspects that must be changed (and in what way) to change the decision. I will illustrate the theory using concrete examples and case studies and also tie these developments to more classical explanations and some widely used approximate explanations. Speaker Bio: Adnan Darwiche is a professor and former chairman of the computer science department at UCLA. He directs the Automated Reasoning Group, which focuses on symbolic reasoning, probabilistic reasoning and their applications to machine learning. Professor Darwiche is a Fellow of AAAI and ACM and recipient of the Lockheed Martin Excellence in Teaching Award. He is a former editor-in-chief of the Journal of Artificial Intelligence Research (JAIR) and author of “Modeling and Reasoning with Bayesian Networks,” by Cambridge University Press. Many of his works can be found on his YouTube Channel, https://www.youtube.com/@UCLA.Reasoning<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.youtube.com%2F%40UCLA.Reasoning&data=05%7C02%7Cai%40engr.oregonstate.edu%7C0714eb3b143f4f9c9c7f08dd3b28bcf0%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638731768330684388%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=fX4XKSjF4ylliu8bJPfQSHXiquZX3hM0HR2pNtxUHbE%3D&reserved=0>. If you would like to meet with the speakers one-on-one, please email tadepall@oregonstate.edu<mailto:tadepall@oregonstate.edu>. For future AI seminars, please visit: https://engineering.oregonstate.edu/EECS/research/AI-seminars<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineering.oregonstate.edu%2FEECS%2Fresearch%2FAI-seminars&data=05%7C02%7Cai%40engr.oregonstate.edu%7C0714eb3b143f4f9c9c7f08dd3b28bcf0%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638731768330699340%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=21bhaNF2XGsGx%2B0gYZuUMtHTHDskurwNi1ifDAd4K9w%3D&reserved=0>. Best, Christian Abou Mrad Graduate Student Oregon State University