论文标题
模型位置和计算反射性：促进数据科学的反射性
Model Positionality and Computational Reflexivity: Promoting Reflexivity in Data Science
论文作者
论文摘要
数据科学和机器学习提供了必不可少的技术，以大规模理解现象，但是在做这项工作时做出的可酌情选择通常不认可。从定性研究实践中，我们描述了如何适应位置和反思性的概念，以提供理解，讨论和披露数据科学工作固有的可支配选择和主观性的框架。我们首先介绍了模型定位和计算反射性的概念，这些概念可以帮助数据科学家反思和传达模型开发和使用的社会和文化背景，数据注释者及其注释以及数据科学家本身。然后，我们将这些概念适应数据科学工作的独特挑战，并提供注释者的指纹和位置挖掘作为有希望的解决方案。最后，我们在一个在线社区中有毒评论的分类器开发的案例研究中证明了这些技术。
Data science and machine learning provide indispensable techniques for understanding phenomena at scale, but the discretionary choices made when doing this work are often not recognized. Drawing from qualitative research practices, we describe how the concepts of positionality and reflexivity can be adapted to provide a framework for understanding, discussing, and disclosing the discretionary choices and subjectivity inherent to data science work. We first introduce the concepts of model positionality and computational reflexivity that can help data scientists to reflect on and communicate the social and cultural context of a model's development and use, the data annotators and their annotations, and the data scientists themselves. We then describe the unique challenges of adapting these concepts for data science work and offer annotator fingerprinting and position mining as promising solutions. Finally, we demonstrate these techniques in a case study of the development of classifiers for toxic commenting in online communities.
