Governments increasingly use algorithmic models to inform their policy making process. Many suggest that employing such quantifications will lead to more efficient, more effective or otherwise better quality policy making. Yet, it remains unclear to what extent these benefits materialize and if so, how they are brought about. This paper draws on the sociology and policy science literature to study how algorithmic models, a particular type of quantification, are used in policy analysis. It presents the outcomes of 38 unstructured interviews with data scientists, policy analysts, and policy makers that work with algorithmic models in government. Based on an in-depth analysis of these interviews, I conclude that the usefulness of algorithmic models in policy analysis is best understood in terms of the commensurability of these quantifications. However, these broad communicative and organizational benefits can only be brought about if algorithmic models are handled with care. Otherwise, they may propagate bias, exclude particular social groups, and will entrench existing worldviews.

政府越来越多地使用算法模型来告知其决策过程。许多人建议采用这样的量化将导致更有效，更有效或质量更高的政策制定。但是，目前尚不清楚这些收益在多大程度上实现，如果实现了，将如何实现。本文利用社会学和政策科学文献来研究算法模型（一种特定的量化类型）如何在政策分析中使用。它显示了与政府中使用算法模型的数据科学家，政策分析师和政策制定者进行的38次非结构化访谈的结果。在对这些访谈进行的深入分析的基础上，我得出结论，就可比性而言，最好地了解算法模型在政策分析中的作用这些量化。但是，只有谨慎处理算法模型，才能带来广泛的沟通和组织利益。否则，他们可能会传播偏见，排斥特定的社会群体，并巩固现有的世界观。