Daniel Serrano
Project: Fairness in Machine Learning
Mentors: Blase Ur, SUPERGroup
Research Area Keywords: Machine Learning & AI //Security & Privacy
Project Description: Daniel Serrano is a sophomore at the University of Chicago majoring in computer science. This summer, he worked with Prof. Blase Ur and Galen Harrison in the SUPERGroup Lab on a project developing a Jupyter plugin called Retrograde that can be used by data scientists to create fairer machine learning (ML) models. Rather than testing the model for fairness after the model is created, Retrograde intervenes during the ML building process helping data scientists to think about and document fairness in relation to the data they are working with. His work this summer consisted of creating a study design to help develop Retrograde with the hopes of collecting and analyzing qualitative data from interviews of different ML stakeholders.