There is growing concern about "algorithmic bias" - that predictive algorithms used in decision-making might bake in or exacerbate discrimination in society. When will these "biases" arise? What should be done about them? We argue that such questions are naturally answered using the tools of...

Machine learning algorithms can find predictive signals that researchers fail to notice; yet they are notoriously hard-to-interpret. How can we extract theoretical insights from these black boxes? History provides a clue. Facing a similar problem - how to extract theoretical insights from their...

There is growing concern about "algorithmic bias" - that predictive algorithms used in decision-making might bake in or exacerbate discrimination in society. We argue that such concerns are naturally addressed using the tools of welfare economics. This approach overturns prevailing wisdom about...

We ask how machine learning can change a crucial step of the scientific process in economics: the advancement of theories through the discovery of "anomalies." Canonical examples of anomalies include the Allais Paradox and the Kahneman-Tversky choice experiments, which are concrete examples of...

We calculate the social return on algorithmic interventions (specifically their Marginal Value of Public Funds) across multiple domains of interest to economists--regulation, criminal justice, medicine, and education. Though these algorithms are different, the results are similar and striking....