Despite (or because of) being a devoted theoretician, I truly enjoy every occasion when theory influences practice. I therefore felt quite a bit of satisfaction when our rather recent work, in the context of algorithmic fairness (with Úrsula Hébert-Johnson, Michael P. Kim and Guy N. Rothblum), found an application for predicting COVID-19 complications. Researchers in Israel, in collaboration with Israel’s biggest health-care provider, adapted a refined model for predicting flu complications to a model for predicting COVID-19 complications. At the time, only very limited data from China were available (marginal statistics). This is where our work came in (following several past empiric studies of the method): the team applied our algorithm to improve the accuracy of predictions across various subpopulations (as part of an immense research and engineering effort). Now that there is (unfortunately) more data, it seems that the predictor exhibited surprisingly good performance (surprising, due to the poor training data). See a manuscript here, an interview here and a more technical talk here (starting at minute 36 roughly). The predictor was applied with the appropriate cautiousness to inform and advise patients.
But this is also an example of the gravity of decisions by researchers and software developers. Taking it to extreme, imagine a predictor that is used to determine which patients are denied treatment in an overwhelmed hospital. The booming research area of algorithmic fairness sees a very short turnover from research ideas (in many areas) to deployment. In an ideal world, it would have been much better to first have a couple of decades to develop the computational foundations of algorithmic fairness, before the practical need arose. But in the real world, the huge scale of algorithmic decision making creates immense demand for solutions. Industry, as well as policy and law makers are unlikely to wait decades or even years, nor is it clear that they should. From my perspective, this reality underscores the urgency for principled and deliberate research – rather than hasty research – continuously developing the foundations of algorithmic fairness and offering answers to real-world challenges.