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 \emph{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.

翻译：算法预测本质上具有不确定性：即使整体准确率相近的模型，对同一对象也可能产生不同预测结果，这引发了对高风险决策可能受建模选择任意性影响的担忧。本文定义"算法敏感性"为任意建模选择影响人类决策的程度，即当同一对象的有利与不利算法预测被呈现给决策者时，决策结果的偏移量。我们在嵌入美国选择性大学招生周期的随机田野实验（n=19,545）中对此进行测量，评审官员审阅每份申请及算法评分时，我们随机将其评分来自两个精度相近的预测模型之一。尽管两个模型整体表现相似，但常对同一申请者给出不同评分，从而产生所展示分数的外生变异。令人意外的是，我们几乎未发现算法敏感性的证据：即使模型间存在显著分歧，展示更有利的评分并未实质性提高申请者的平均录取概率。这些结果表明，在这一专家主导的高风险场景中，人类决策对算法预测的任意性变化基本保持稳定，凸显了专业裁量权与制度环境在调节算法不确定性下游影响中的关键作用。