Critique and Contribute: A Practice-Based Framework for Improving Critical Data Studies and Data Science
Critique and Contribute: A Practice-Based Framework for Improving Critical Data Studies and Data Science
复制标题
批评与贡献：改进关键数据研究和数据科学的基于实践的框架
作者：
Gina Neff;A. Tanweer;Brittany Fiore;Laura Osburn
如果数据科学的主要批评者参与进来帮助改进它，数据科学会是什么样子？如果采用考虑数据科学日常实践的方法，数据科学的批评会如何改进？本文主张学者们在寻求批判数据科学的对话和寻求推进数据科学实践的对话之间架起桥梁，以确定和创建更符合伦理的数据科学所需的社会和组织安排。我们总结了在关键数据研究中常见的四种批评：数据本质上是解释性的，数据与上下文密不可分，数据是通过产生它们的社会物质安排来介导的，数据是价值观谈判和沟通的媒介。我们与学术数据科学家、“数据向善”项目和专业的跨学科工程团队一起进行定性研究，以证明数据科学家在承认和应对工作复杂性时的日常经验中存在这些批评。使用来自两个大型多研究人员现场的人种学插图，我们开发了一套用于分析和推进数据科学实践和改进关键数据研究的概念，包括（1）沟通是数据科学奋进的核心;（2）理解数据是一个集体过程;（3）数据是起点，而不是终点;（4）数据是故事集。最后，我们呼吁数据科学和关键数据研究领域的研究人员和从业人员采取行动。首先，创造机会将社会科学和人文专业知识同时引入数据科学实践，将推动数据科学和关键数据研究。其次，从业者应该利用关键数据研究的见解来建立新型的组织安排，我们认为这将有助于推动更符合道德的数据科学。参与关键数据研究的见解将改善数据科学。仔细关注数据科学的实践将改善学术批评。这些不同社区之间真正的合作对话将有助于推动更道德、更好的认知方式，在数据日益饱和的社会中。
Abstract 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.