“You Social Scientists Love Mind Games”: Experimenting in the “divide” between data science and critical algorithm studies
“You Social Scientists Love Mind Games”: Experimenting in the “divide” between data science and critical algorithm studies
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“你们社会科学家喜欢智力游戏”：在数据科学和批判算法研究之间的“鸿沟”中进行实验
作者：
David Moats;Nick Seaver
近年来，许多定性社会学家、人类学家和社会理论家从认识论和政治角度批评了数据科学中涉及的算法和其他自动化过程的使用。然而，事实证明，很难将这些重要的见解带入数据科学本身的实践中。我们认为，这个问题的一部分与关于这两个领域之间关系的未被充分研究或未被承认的假设有关，即关于数据科学及其批评者如何能够和应该联系起来的想法。受最近科学技术研究干预工作的启发，我们试图举办一次会议，要求实践数据科学家使用文本分析工具（如共词和主题建模）分析有关其工作的批判性社会科学文献语料库。这样做的目的是要引起对这些案文的内容和这种分析的可能局限性的讨论。在这篇评论中，我们反思了实验的规划阶段，以及数据科学家和定性社会科学家对实验的反应，揭示了不同领域规范立场之间的一些紧张关系和相互作用。我们主张进一步的研究，这可以帮助我们了解这些跨学科的紧张局势打开，不纸，但也不把它们作为给定的。
In recent years, many qualitative sociologists, anthropologists, and social theorists have critiqued the use of algorithms and other automated processes involved in data science on both epistemological and political grounds. Yet, it has proven difficult to bring these important insights into the practice of data science itself. We suggest that part of this problem has to do with under-examined or unacknowledged assumptions about the relationship between the two fields—ideas about how data science and its critics can and should relate. Inspired by recent work in Science and Technology Studies on interventions, we attempted to stage an encounter in which practicing data scientists were asked to analyze a corpus of critical social science literature about their work, using tools of textual analysis such as co-word and topic modelling. The idea was to provoke discussion both about the content of these texts and the possible limits of such analyses. In this commentary, we reflect on the planning stages of the experiment and how responses to the exercise, from both data scientists and qualitative social scientists, revealed some of the tensions and interactions between the normative positions of the different fields. We argue for further studies which can help us understand what these interdisciplinary tensions turn on—which do not paper over them but also do not take them as given.