Fairness in Machine Learning in Health
Dr. Marzyeh Ghassemi (University of Toronto) and Dr. Kadija Ferryman (NYU Tandon School of Engineering) hosted an interdisciplinary gathering of scholars focused on the topic of fairness in machine learning in health at Data & Society Research Institute.
Fairness in machine learning in health has been a growing area of interest in academic research, including identifying problems as well as proposing remedies from both technical and ethical and social scientific standpoints. The overarching goal of this meeting was to bring social, scientific, and technical perspectives together to build an interdisciplinary community of researchers on fairness and machine learning in health.
Additional goals for this meeting included:
1) Gathering evidence for a white paper that details a set of interdisciplinary priorities and interventions for machine learning and health;
2) Creating a conference proposal(s) on this topic.
The outcomes are intended to catalyze the ML, fairness, and health community across disciplines and set an agenda that will guide future work.