What Do Algorithmic Fairness and COVID-19 Case-Severity Prediction Have in Common?

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United States Simons Institute for the Theory of Computing
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Published on ● Video Link: https://www.youtube.com/watch?v=r_alyuZULYI


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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

“Multicalibration: Calibration for the (Computationally-Identifiable) Masses,” by Úrsula Hébert-Johnson, Michael P. Kim, Omer Reingold, Guy N. Rothblum [https://arxiv.org/abs/1711.08513]

“Addressing Bias in Prediction Models by Improving Subpopulation Calibration,” Noam Barda, Noa Dagan, Guy N. Rothblum, Gal Yona, Eitan Bachmat, Philip Greenland, Morton Leibowitz, Ran Balicer [under submission]

COVID-19 collaboration [https://www.themarker.com/news/health/1.8708713]:

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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Tags:
Simons Institute
theoretical computer science
UC Berkeley
Computer Science
Theory of Computation
Theory of Computing
COVID-19
coronavirus
algorithmic science
fairness
Guy Rothblum
Shafi Goldwasser
polylogues