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.

翻译：算法预测本质上具有不确定性：即使总体准确率相近的模型，也可能对同一个体产生不同的预测结果，这引发了人们对高风险决策可能受任意建模选择影响的担忧。本文定义算法依赖度为决策结果在多大程度上取决于向决策者呈现的是更有利还是较不利的算法预测。我们通过一项嵌入美国选择性大学招生流程的随机现场实验（样本量=19,545）对此进行估计：招生官员在审阅每份申请材料时，会同时查看算法评分，而我们随机分配该评分来自两个准确率相近的预测模型之一。尽管两个模型在总体表现上相似，但它们经常对同一申请人给出不同评分，从而为所显示的评分创造了外生变异。令人惊讶的是，我们几乎没有发现算法依赖的证据：即使当模型预测存在显著分歧时，呈现更有利的评分也并未显著提高申请人的平均录取概率。这些发现表明，在此类专家主导的高风险决策场景中，人类决策行为基本不受算法预测任意变异的影响，这凸显了专业判断力与制度环境在调节算法不确定性下游效应中的关键作用。