Machine learning algorithms are now capable of performing evaluations previously conducted by human experts (e.g., medical diagnoses). How should we conceptualize the difference between evaluation by humans and by algorithms, and when should an individual prefer one over the other? We propose a framework to examine one key distinction between the two forms of evaluation: Machine learning algorithms are standardized, fixing a common set of covariates by which to assess all individuals, while human evaluators customize which covariates are acquired to each individual. Our framework defines and analyzes the advantage of this customization -- the value of context -- in environments with high-dimensional data. We show that unless the agent has precise knowledge about the joint distribution of covariates, the benefit of additional covariates generally outweighs the value of context.

翻译：机器学习算法现已能够执行以往由人类专家进行的评估（例如医学诊断）。我们应如何概念化人类评估与算法评估之间的差异，以及在何种情况下个体应倾向于选择其中一种？我们提出一个框架来检验两种评估形式之间的关键区别：机器学习算法是标准化的，通过固定的协变量集合评估所有个体；而人类评估者则根据每个个体定制化获取协变量。我们的框架在高维数据环境中定义并分析了这种定制化优势——即情境价值。研究表明，除非智能体能精确掌握协变量的联合分布，否则额外协变量的收益通常高于情境价值。