The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations
Aparna Balagopalan
Aparna Balagopalan is a fourth 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 third 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 University of Toronto and Vector Institute visiting MIT. Her research focuses on understanding how self-supervised pre-training strategies represent data to build models that generalize well out-of-distribution, and developing methods that enable efficient and reliable adaptation. She is also interested in leveraging properties of large models for reasoning and robustness to distribution shifts.