Explanation and Fairness in Unsupervised Learning

August 30, 3:30 pm - 5:00 pm (CEST)

Speakers: Ian Davidson

Tutorial website: https://www.cs.ucdavis.edu/~davidson/ECAI2020/overview.htm

Agenda: Explanation and Fairness in Unsupervised Learning

Unsupervised learning approaches such as clustering are extensively used in AI with explanation and fairness emerging as important challenges. We cover explainable and fair unsupervised learning from multiple perspectives such as the philosophical under-pinnings, algorithmic details and application areas.

Ian Davidson: I am a Professor in the department of computer science at the University of California, Davis since 2009 (and before that an assistant and associated Professor). I teach AI, ML and for the past decade a course on ethics and AI/Technology. My research is a mix of fundamental algorithmic contributions to unsupervised learning (clustering and outlier detection) and supervised learning (transfer, active learning) and more practical applications (with others) in neuroscience and intelligent tutoring. With regard to fairness and explanation in unsupervised learning, I have published papers at NIPS 2018, IJCAI 2018, AAAI 2020 and ECAI 2020 on the topic and I have grants from NSF on core research on the topic and NIH to apply XAI to precision medicine. My website is here my google scholar page is here.