数据集有政治吗？计算机视觉数据集开发中的学科价值
Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development
摘要：
数据是机器学习的重要组成部分。该领域依赖于数据来训练、验证和测试模型。随着技术能力的提高，机器学习研究在学术和行业环境中蓬勃发展，其中一个主要焦点是计算机视觉。计算机视觉是机器学习的一个流行领域，越来越多地与现实世界的应用相关，从警务中的面部识别到自动驾驶车辆的目标检测。鉴于计算机视觉倾向于塑造机器学习研究并影响人类生活，我们试图了解围绕数据集文档的学科实践-数据如何收集，策划，注释和打包成数据集，供计算机视觉研究人员和从业者用于模型调整和开发。具体来说，我们研究了什么数据集文档传达了视觉数据的潜在价值以及计算机视觉作为一个领域的更大实践和目标。为了进行这项研究，我们收集了大约500个计算机视觉数据集的语料库，从中我们在不同的视觉任务中抽取了114个数据集出版物。通过结构化和主题内容分析，我们记录了一些关于公认的数据实践的价值观，是什么使数据成为理想的数据，以及在数据集构建过程中对人类的处理。我们讨论了计算机视觉数据集作者如何以牺牲护理为代价来重视效率;以牺牲上下文为代价来重视普遍性;以牺牲位置性为代价来重视公正性;以牺牲数据工作为代价来重视模型工作。我们发现的许多沉默的价值观与社会计算实践相对立。最后，我们提出了如何更好地将沉默值纳入数据集创建和管理过程的建议。
英文摘要：
Data is a crucial component of machine learning. The field is reliant on data to train, validate, and test models. With increased technical capabilities, machine learning research has boomed in both academic and industry settings, and one major focus has been on computer vision. Computer vision is a popular domain of machine learning increasingly pertinent to real-world applications, from facial recognition in policing to object detection for autonomous vehicles. Given computer vision's propensity to shape machine learning research and impact human life, we seek to understand disciplinary practices around dataset documentation - how data is collected, curated, annotated, and packaged into datasets for computer vision researchers and practitioners to use for model tuning and development. Specifically, we examine what dataset documentation communicates about the underlying values of vision data and the larger practices and goals of computer vision as a field. To conduct this study, we collected a corpus of about 500 computer vision datasets, from which we sampled 114 dataset publications across different vision tasks. Through both a structured and thematic content analysis, we document a number of values around accepted data practices, what makes desirable data, and the treatment of humans in the dataset construction process. We discuss how computer vision datasets authors value efficiency at the expense of care; universality at the expense of contextuality; impartiality at the expense of positionality; and model work at the expense of data work. Many of the silenced values we identify sit in opposition with social computing practices. We conclude with suggestions on how to better incorporate silenced values into the dataset creation and curation process.
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