The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations
Aparna Balagopalan
Aparna Balagopalan is a fifth year PhD student in EECS at MIT. Her research broadly focuses on developing fair and robust models by re-evaluating and surfacing assumptions in machine learning-based measurements in socially-relevant contexts like healthcare. Prior to this, she received a Master’s degree from the University of Toronto and a BTech degree from IIT Guwahati. She currently holds an Amazon Doctoral Fellowship from MIT’s Science Hub.
Haoran Zhang
Haoran is a fourth year PhD student in EECS at MIT. He is generally interested in building robust machine learning models that maintain their performance and fairness across out-of-distribution environments, as well as applying such models to the healthcare setting. Haoran previously received his M.Sc. at the University of Toronto under the co-supervision of Dr. Marzyeh Ghassemi and Dr. Quaid Morris, and his B.Eng. from McMaster University.
Kimia Hamidieh
Kimia is a PhD student at MIT EECS. Her research focuses on understanding how self-supervised pre-training strategies represent data to build models that generalize well out-of-distribution, as well as developing post-training strategies that ensure safety of models. She previously received her MSc at the University of Toronto and her BSc from Sharif University of Technology.