Ava Soleimany and other MIT CSAIL researchers propose automated method for debiasing AI algorithms

January 30, 2019

In a paper (“Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure“) scheduled to be presented at the Association for the Advancement of Artificial Intelligence’s conference on Artificial Intelligence, Ethics, and Society in Honolulu this week, MIT CSAIL scientists describe an AI system that can automatically “debias” data by resampling it to be more balanced. They propose that, when evaluated on a dataset specifically designed to test for biases in computer vision systems, it demonstrated both superior performance and “decreased categorical bias.” You can access the publication here.

Alexander Amini and fellow Ph.D. student (and Bhatia lab member) Ava Soleimany contributed to the new paper, along with graduate student Wilko Schwarting and MIT professors Sangeeta Bhatia and Daniela Rus.

Additional press coverage can be found below: