CARE-AI Seminar Series: Dr. Eran Tal (Virtual)
The accuracy and fairness of machine learning models are often assumed to be separate, and to some extent conflicting, desiderata. I argue that this is a mistake that partially stems from an overly simplistic understanding of accuracy. Using insights from the philosophy of measurement, I distinguish among three sources of inaccuracy in supervised machine learning: label bias, modeling bias, and fitness bias. While the machine learning community has focused on minimizing label and modeling bias, it has tended to shift responsibility for fitness bias to users.