Episode 104: Victoria Angelova on the Comparison between Algorithms and Humans Making Decisions

Victoria Angelova is a PhD Candidate at Harvard University

The paper discussed in this episode is this one.

This paper studies whether human decision-makers can improve on algorithmic recommendations, in the setting of bail decisions in the criminal justice system. Bail judges receive recommendations from predictive algorithms and can choose to confirm or override them. The paper finds that most judges do not add value beyond the algorithm, although a minority do.

Other articles discussed in this episode are:

ARNOLD, D., DOBBIE, W. and YANG, C. S. (2018), “Racial Bias in Bail Decisions”, Quarterly Journal of Economics, 133, 1885–1932.

BONHOMME, S. and WEIDNER, M. (2022), “Posterior Average Effects”, Journal of Business & Economic Statistics, 40, 1849–1862.

HOFFMAN, M., KAHN, L. B. and LI, D. (2018), “Discretion in Hiring”, Quarterly Journal of Economics, 133, 765–800.

HULL, P. (2020), “Estimating Hospital Quality with Quasi-Experimental Data” (Unpublished Working Paper)

KLEINBERG, J., LAKKARAJU, H., LESKOVEC, J., et al. (2018), “Human Decisions and Machine Predictions”, Quarterly Journal of Economics, 133, 237–293.