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Throughout the social world, predictive algorithms are a means to an end. They provide forecasts of future events with the aim to improve human decisions and drive positive changes in core life outcomes (increase graduation rates, life expectency, etc.). Given that higher welfare — not accuracy — is the ultimate goal of prediction, it’s clear that algorithms are just a small piece of the puzzle. There are many things we can do to improve welfare beyond improving the accuracy of predictive systems. Given this broad design space, when is investing in prediction truly “worth it”? This talk will discuss a new line of research that aims to formalize foundations for this question. Based on joint work with Christoph Kern and Unai Fischer-Abaigar.
