The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations

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Aparna Balagopalan
Aparna Balagopalan

Aparna Balagopalan is a second year PhD student at the University of Toronto and Vector Institute, co-supervised by Prof. Marzyeh Ghassemi and Prof. Frank Rudzicz. Her research focuses on developing interpretable and justifiable machine learning models for healthcare. Prior to this, she received a Master’s degree from the University of Toronto, supported by a MITACS Accelerate scholarship, and a BTech degree from the IIT Guwahati. She currently holds a DeepMind PhD Fellowship for 2020-2021.

Haoran Zhang
Haoran Zhang

Haoran is a first-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 Hamidieh

Kimia is a Ph.D. student in Computer Science at U of T and Vector Institute. Her research interests involve devising strategies for learning fair representations with respect to certain protected attributes from imbalanced and long-tailed distribution data. Currently, she is conducting research focusing on fairness in self-supervised representation learning. Previously, she graduated from Sharif University of Technology with a B.Sc. in Computer Engineering and interned at IST Austria.

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