1 Oxford Internet Institute, University of Oxford , Oxford, United Kingdom .
2 Department of Communication, University of Washington , Seattle, Washington.
Big Data. 2017 Jun;5(2):85-97. doi: 10.1089/big.2016.0050.
What would data science look like if its key critics were engaged to help improve it, and how might critiques of data science improve with an approach that considers the day-to-day practices of data science? This article argues for scholars to bridge the conversations that seek to critique data science and those that seek to advance data science practice to identify and create the social and organizational arrangements necessary for a more ethical data science. We summarize four critiques that are commonly made in critical data studies: data are inherently interpretive, data are inextricable from context, data are mediated through the sociomaterial arrangements that produce them, and data serve as a medium for the negotiation and communication of values. We present qualitative research with academic data scientists, "data for good" projects, and specialized cross-disciplinary engineering teams to show evidence of these critiques in the day-to-day experience of data scientists as they acknowledge and grapple with the complexities of their work. Using ethnographic vignettes from two large multiresearcher field sites, we develop a set of concepts for analyzing and advancing the practice of data science and improving critical data studies, including (1) communication is central to the data science endeavor; (2) making sense of data is a collective process; (3) data are starting, not end points, and (4) data are sets of stories. We conclude with two calls to action for researchers and practitioners in data science and critical data studies alike. First, creating opportunities for bringing social scientific and humanistic expertise into data science practice simultaneously will advance both data science and critical data studies. Second, practitioners should leverage the insights from critical data studies to build new kinds of organizational arrangements, which we argue will help advance a more ethical data science. Engaging the insights of critical data studies will improve data science. Careful attention to the practices of data science will improve scholarly critiques. Genuine collaborative conversations between these different communities will help push for more ethical, and better, ways of knowing in increasingly datum-saturated societies.
如果让数据科学的主要批评者参与进来，帮助改进数据科学，那么数据科学会是什么样子？如果采用一种考虑数据科学日常实践的方法，对数据科学的批评会如何改进？本文认为，学者们应该弥合那些试图批评数据科学的对话和那些试图推进数据科学实践的对话，以确定并创造出更具道德的数据科学所需的社会和组织安排。我们总结了在批判性数据研究中常见的四个批评：数据本质上是解释性的，数据与语境密不可分，数据是通过产生它们的社会物质安排来中介的，以及数据是价值协商和沟通的媒介。我们展示了与学术数据科学家、“数据向善”项目和专门的跨学科工程团队的定性研究，以表明这些批评在数据科学家的日常经验中是存在的，因为他们承认并应对工作的复杂性。通过来自两个大型多研究员实地研究地点的民族志案例，我们开发了一组概念，用于分析和推进数据科学实践，并改进批判性数据研究，包括：（1）沟通是数据科学工作的核心；（2）理解数据是一个集体过程；（3）数据是起点，而不是终点；（4）数据是一系列的故事。最后，我们为数据科学和批判性数据研究的研究人员和从业者提出了两个行动呼吁。首先，为数据科学实践创造机会，同时引入社会科学和人文科学专业知识，将同时推进数据科学和批判性数据研究。其次，从业者应该利用批判性数据研究的见解来构建新的组织安排，我们认为这将有助于推进更具道德的数据科学。将批判性数据研究的见解纳入数据科学将改进数据科学。仔细关注数据科学的实践将改进学术批评。这些不同社区之间真正的合作对话将有助于推动在数据日益饱和的社会中，以更道德、更好的方式进行认知。
