“I believe the scientific farmer is going to be the farmer of the future. […] If farming is to be made a success in this country, it has to be done on a scientific basis.”
— J.B. Reynolds (1867-1948)
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.
Thursday, September 30th, 2021 8:00 AM to 10:00 AM
Join students, faculty and staff as we come together in ceremony honouring Residential School Survivors and the children who never returned home. The ceremony will be led by Elders Dan and Mary Lou Smoke and hosted by the Indigenous Student Centre and Indigenous Initiatives and in collaboration with the Indigenous Student Society. All students, faculty and staff are welcome and may wish to attend for the opening at noon or to drop by as they can.