ISyE Seminar Series: Alex Albright
"The Hidden Effects of Algorithmic Recommendations"
Research Economist, Opportunity & Inclusive Growth Institute
Federal Reserve Bank of Minneapolis
About the seminar:
Algorithms inform human decisions in many high-stakes settings. They provide decision-makers with predictions concerning the probability of an event. However, there is typically an additional step involved: decision-makers are recommended particular decisions based on the predictions. I isolate the causal effects of these algorithmic recommendations by leveraging a setting in which the recommendations given to bail judges changed, but the algorithm’s predictions given to judges did not. Recommendations significantly impacted decisions: lenient recommendations increased lenient bail decisions by over 50% for marginal cases. I explore possible mechanisms behind this effect and provide evidence that recommendations can change the costs of errors to decision-makers. Judges may be more lenient when their choices are consistent with recommendations because the recommendation can shield them from political backlash. Finally, I show that variation in adherence to recommendations complicates how algorithm-based systems affect racial disparities. Judges are more likely to deviate from lenient recommendations for Black defendants than for white defendants with identical algorithmic risk scores.
Alex Albright is a Research Economist with the Opportunity & Inclusive Growth Institute at the Federal Reserve Bank of Minneapolis. She studies law and economics, the economics of the justice system, and economic inequality in the US. She received her PhD in Economics from Harvard University in 2022. During the PhD, Alex was a Stone PhD Scholar in Harvard’s Inequality & Social Policy Program, a Considine Fellow at Harvard Law School, and a Horowitz Foundation Grantee. Prior to graduate school, Alex was a Research Fellow at Stanford Law School. She received her BA in Mathematics and Economics from Williams College.