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 sensitivity as the extent to which arbitrary modeling choices propagate into human decisions: how much a decision outcome shifts when a more favorable versus less favorable algorithmic prediction is presented to the decision-maker for the same individual. 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 sensitivity: 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.

Using a sample of 883 Latiné engineering students, the present study explored the measurement characteristics of the Negative Outcome Expectations Scale for Engineers (NOES-E), applying advanced psychometric techniques, including bifactor and the exploratory structural equation modeling (ESEM) framework, to ascertain the scale's most suitable factor structure. Additionally, measurement invariance across Latino and Latina participants was tested to investigate if the scale operates equally across gender groups and whether the latent mean scores are invariant. The results suggested that the bi-factor ESEM provided the best model fit when contrasted with other possible factor structures. The findings generally highlighted the general factor as reflecting the negative outcome expectations, suggesting that the use of total score is advised over the use of individual subscale scores. Findings from the measurement invariance testing across men and women indicated the NOES-E score can be compared between both groups.