Julio Ramirez

Project: Fairness in Machine Learning

Mentors/Lab: Prof. Blase Ur, Julia Hanson (BS 2018), Galen Harrison (PhD, CS)/SUPERGroup

Research Area Keywords: Machine Learning // Human-Computer Interaction

I worked on the Fairness in Machine Learning project with SUPERgroup. We focused on analyzing empirical data to understand what people perceived to determine a fair machine learning model, among other things. I programmed in JavaScript to help work on a process visualization as part of the project. I also used JavaScript to try and develop summary statistics of the data that was collected as part of our project. I also did qualitative coding for the survey responses used in the project.

Project News: The paper that grew out of this research, titled “An Empirical Study on the Perceived Fairness of Realistic, Imperfect Machine Learning Models”, was accepted at the ACM FAT* 2020 Conference.