Isomorphism through algorithms: Institutional dependencies in the case of Facebook
Isomorphism through algorithms: Institutional dependencies in the case of Facebook
复制标题
通过算法实现同构：以 Facebook 为例的机构依赖性
作者：
R. Caplan;D. Boyd
算法和数据驱动的技术正越来越多地被各种不同的部门和机构接受。这篇白皮书考察了像Facebook这样的组织制定的算法和数据驱动技术如何在一个行业内引发相似性。利用组织社会学和新制度主义的理论，本文追溯了大数据和算法的官僚根源，以检验通过数据驱动和算法逻辑产生并中介的制度依赖。这种类型的分析揭示了组织上下文是如何嵌入到算法中的，然后这些算法可以嵌入到其他组织和个人实践中。通过调查作为组织和官僚机构的技术做法，可以重新讨论问责和决策。
Algorithms and data-driven technologies are increasingly being embraced by a variety of different sectors and institutions. This paper examines how algorithms and data-driven technologies, enacted by an organization like Facebook, can induce similarity across an industry. Using theories from organizational sociology and neoinstitutionalism, this paper traces the bureaucratic roots of Big Data and algorithms to examine the institutional dependencies that emerge and are mediated through data-driven and algorithmic logics. This type of analysis sheds light on how organizational contexts are embedded into algorithms, which can then become embedded within other organizational and individual practices. By investigating technical practices as organizational and bureaucratic, discussions about accountability and decision-making can be reframed.