Michael Saxon

Michael Saxon is a Siegel Family Endowment Fellow and Postdoctoral Scholar in the Tech Policy Lab at the University of Washington. He works on evaluating artificial intelligence systems, particularly language models and text-to-image models, for technical performance and social impacts. He likes to build new metrics to assess previously unmeasurable aspects of these systems, particularly with respect to multilingual and multicultural performance and impact on human behavior. He completed a Ph.D. in Computer Science in the Natural Language Processing group at the University of California, Santa Barbara, advised by Prof. William Wang as an NSF Graduate Research Fellow. He received his BS and MS degrees in Electrical and Computer Engineering at Arizona State University. Since 2020 he has authored refereed first-author publications in top machine learning, NLP, and speech venues, including one spotlight paper, and his research has been covered in TechCrunch and IEEE Spectrum.