MIT researchers find that the explanation methods designed to help users determine whether to trust a machine-learning model’s predictions can perpetuate biases and lead to worse outcomes for people from disadvantaged groups. Credit: Jose-Luis Olivares, MIT with images from iStockphoto
However, are these explanation methods fair? If an explanation method provides better approximations for men than for women, or for white people than for black people,users may be more inclined to trust the model’s predictions for some people but not for others.scientists carefully examined the fairness of some widely used explanation methods.
Balagopalan wrote the paper with CSAIL graduate students Haoran Zhang and Kimia Hamidieh; CSAIL postdoc Thomas Hartvigsen; Frank Rudzicz, associate professor of computer science at the University of Toronto; and senior author Marzyeh Ghassemi, an assistant professor and head of the Healthy ML Group. The research will be presented at the ACM Conference on Fairness, Accountability, and Transparency.
With these metrics, they searched for fidelity gaps using two types of explanation models that were trained on four real-world datasets for high-stakes situations, such as predicting whether a patient dies in the ICU, whether a defendant reoffends, or whether a law school applicant will pass the bar exam. Each dataset contained protected attributes, like the sex and race of individual people.
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