Dalla Lana School of Public Health

Links

• https://canssiontario.utoronto.ca/?mec-events=ares-kosuke_imai

We know that human bias influences the fairness of algorithms. How do algorithmic recommendations impact human decisions? Can algorithms improve the fairness of human decisions?

Join us on December 14, as Dr. Kosuke Imai, Professor in the Departments of Government and of Statistics at Harvard University, explores the statistical models and machine learning algorithms that help guide the decisions of humans and whether machine-made recommendations improve human-decision making.

Full Abstract:
Despite an increasing reliance on fully-automated algorithmic decision making in our day-to-day lives, human beings still make highly consequential decisions. As frequently seen in business, healthcare, and public policy, recommendations produced by statistical models and machine learning algorithms are provided to human decision-makers in order to guide their decisions. While there exists a fast growing literature evaluating the bias and fairness of such algorithms, an overlooked question is whether they help humans make better decisions. Using the concept of principal stratification from the causal inference literature, we develop a statistical methodology for experimentally evaluating the causal impacts of algorithmic recommendations on human decisions. We also show how to examine whether algorithmic recommendations improve the fairness of human decisions. We apply the proposed methodology to the original randomized evaluation of pretrial public safety assessment (PSA) in the US criminal justice system. A goal of the PSA is to help a judge decide which arrested individuals should be released. We randomize whether a judge is presented with a PSA and investigate how the PSA provision influences judge’s decisions and arrestees’ subsequent behavior.

Before moving to Harvard in 2018, Imai taught at Princeton University for 15 years where he was the founding director of the Program in Statistics and Machine Learning. In addition, Imai served as the President of the Society for Political Methodology from 2017 to 2019 and was elected fellow in 2017. He has been Professor of Visiting Status in the Faculty of Law and Graduate Schools of Law and Politics at the University of Tokyo.

ARES, the Data Science Applied Research and Education Seminar, is a collaboration between the Department of Statistical Sciences at U of T and CANSSI Ontario.