no code implementations • 5 Oct 2021 • Laura Blattner, Scott Nelson, Jann Spiess
We show how to optimally regulate prediction algorithms in a world where an agent uses complex 'black-box' prediction functions to make decisions such as lending, medical testing, or hiring, and where a principal is limited in how much she can learn about the agent's black-box model.
no code implementations • 17 May 2021 • Laura Blattner, Scott Nelson
We show that lenders face more uncertainty when assessing default risk of historically under-served groups in US credit markets and that this information disparity is a quantitatively important driver of inefficient and unequal credit market outcomes.
