Dr. Michael Quinn on Algorithmic Bias
Building an unbiased AI system is easier said than done.
In his talk “Ten Things You Need to Know about Algorithmic Bias,” Michael J. Quinn, Ph.D. provided important examples of AI-driven systems making biased recommendations. The lecture was the keynote of Gonzaga’s Engineers Week celebrations.
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, gaps in the data used to train these algorithms lead to biased systems.
Dr. Quinn explored traits of data sets that caused specific examples of bias in computerized systems. He demonstrated how a system considered to be “fair” according to one reasonable metric would be judged “unfair” according to another reasonable metric.
Dr. Quinn earned his Bachelor of Science in Mathematics at Gonzaga before launching an academic career in computer science. He earned his Ph.D. from Washington State University and held faculty positions at the University of New Hampshire and Oregon State University. He conducted pioneering research in parallel computing before shifting his focus to computer ethics. His 2004 textbook, Ethics for the Information Age, explores moral problems related to modern uses of information technology such as privacy, intellectual property rights, and computerized system failures. Dr. Quinn most recently served as the Dean of the College of Science and Engineering at Seattle University for 15 years before retiring in 2022.
