Renouard Lecture: Michael Quinn on Ethics of AI Algorithms

About This Event

This special Renouard Lecture is the keynote of Gonzaga's Engineers Week!

• Come to the reception at 4 p.m. in your best Zag gear to be in the SEAS Gonzaga Day video!
• Lecture begins at 4:30 p.m.  All majors welcome!

Ten Things You Need to Know about Algorithmic Bias

AI-driven systems have made biased recommendations about who should be prioritized for preventative health care, who is eligible to receive a loan, and whose neighborhoods should be targeted with additional police patrols. Learn what causes AI-driven systems to be biased and what people can - and can't - do to ensure these systems make fair decisions.

About the Speaker

Dr. Michael J. Quinn is a computer scientist and author. He did pioneering research in the field of parallel computing, and his textbooks on that subject have been used by hundreds of universities worldwide. In the early 2000s his focus shifted to computer ethics, and in 2004 he published a textbook, Ethics for the Information Age, that explores moral problems related to modern uses of information technology, such as privacy, intellectual property rights, computer security, computerized system failures, and the relationship between automation and unemployment. The book, now in its ninth edition, has been adopted by more than 250 colleges and universities in the United States and many more internationally.
A native Oregonian, he spent most of his early years in the Gresham area. After graduating from Gresham High School, he earned a B.S. in mathematics from Gonzaga University and an M.S. in computer sciences from the University of Wisconsin-Madison. He worked for two years as a software engineer at Tektronix, Inc., in Wilsonville and Beaverton, Oregon, then returned to graduate school to complete a Ph.D. in computer science from Washington State University. During his academic career he was a computer science professor at the University of New Hampshire and Oregon State University and a college dean at Seattle University. Now retired from full-time work, he lives in the Portland, Oregon metropolitan area.

Official Lecture Abstract

AI-driven systems are deployed in many domains where consequential decisions need to be made. They have recommended who should be prioritized for preventative health care, who is eligible to receive a loan, and whose neighborhood should be targeted with additional police patrols. Unfortunately, computerized systems operating in these and other domains have been found to be biased. It turns out that creating an unbiased AI-driven system is much easier said than done. In this talk, I provide important examples of AI-driven systems making biased recommendations, discuss the causes of bias in computerized systems, and demonstrate how a system that is considered to be fair according to one reasonable metric may be judged unfair according to another reasonable metric.