Does Algorithmic Uncertainty Sway Human Experts? Evidence from a Field Experiment in Selective College Admissions
Hansol Lee, AJ Alvero, René F. Kizilcec, Thorsten Joachims
TL;DR
The paper investigates whether predictive multiplicity in algorithmic assessments sways human experts in high-stakes decisions. It implements a randomized field experiment in a selective college admissions context, presenting reviewers with one of two equally accurate scoring systems for each applicant. Despite substantial individual-level disagreement between the scoring systems, the study finds little evidence that presenting a more favorable score systematically changes admission outcomes, suggesting robust human discretion and institutional norms buffer the effects of algorithmic uncertainty. The results imply that the risks of arbitrariness from predictive multiplicity depend on how algorithmic advice is embedded within decision processes, with calibrated discretion in this high-stakes setting potentially mitigating adverse downstream effects.
Abstract
Algorithmic predictions are inherently uncertain: even models with similar aggregate accuracy can produce different predictions for the same individual, raising concerns that high-stakes decisions may become sensitive to arbitrary modeling choices. In this paper, we define algorithmic reliance as the extent to which a decision outcome depends on whether a more favorable versus less favorable algorithmic prediction is presented to the decision-maker. We estimate this in a randomized field experiment (n=19,545) embedded in a selective U.S. college admissions cycle, in which admissions officers reviewed each application alongside an algorithmic score while we randomly varied whether the score came from one of two similarly accurate prediction models. Although the two models performed similarly in aggregate, they frequently assigned different scores to the same applicant, creating exogenous variation in the score shown. Surprisingly, we find little evidence of algorithmic reliance: presenting a more favorable score does not meaningfully increase an applicant's probability of admission on average, even when the models disagree substantially. These findings suggest that, in this expert, high-stakes setting, human decision-making is largely invariant to arbitrary variation in algorithmic predictions, underscoring the role of professional discretion and institutional context in mediating the downstream effects of algorithmic uncertainty.