What Do Algorithmic Fairness and COVID-19 Case-Severity Prediction Have in Common? | Simons Institute Polylogues

In this episode of Simons Institute Polylogues, Shafi Goldwasser (Director, Simons Institute) interviews Guy Rothblum (Weizmann Institute) about a new research collaboration applying techniques from the field of algorithmic fairness to determine which patients are most likely to develop severe cases of COVID-19.

REFERENCES

1. “Multicalibration: Calibration for the (Computationally-Identifiable) Masses,” by Úrsula Hébert-Johnson, Michael P. Kim, Omer Reingold, and Guy N. Rothblum.
2. “Addressing Bias in Prediction Models by Improving Subpopulation Calibration,” by Noam Barda, Noa Dagan, Guy N. Rothblum, Gal Yona, Eitan Bachmat, Philip Greenland, Morton Leibowitz, and Ran Balicer [under submission].
3. COVID-19 collaboration. Clalit Research Institute: Adi Berliner, Amichai Akriv, Anna Kuperberg, Dan Riesel, Daniel Rabina, Galit Shaham, Ilan Gofer, Mark Katz, Michael Leschinski, Noa Dagan, Noam Barda, Oren Auster, Reut Ohana, Shay Ben-Shachar, Shay Perchik Uriah Finkel, Yossi Levi. Technion: Daniel Greenfeld, Uri Shalit, Jonathan Somer. Weizmann Institute: Guy Rothblum, Gal Yona.

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