All datasets emerge from and are enmeshed in power-laden semiotic systems. While emerging data ethics curriculum is supporting data science students in identifying data biases and their consequences, critical attention to the cultural histories and vested interests animating data semantics is needed to elucidate the assumptions and political commitments on which data rest, along with the externalities they produce. In this article, I introduce three modes of reading that can be engaged when studying datasets—a denotative reading (extrapolating the literal meaning of values in a dataset), a connotative reading (tracing the socio-political provenance of data semantics), and a deconstructive reading (seeking what gets Othered through data semantics and structure). I then outline how I have taught students to engage these methods when analyzing three datasets in Data and Society—a course designed to cultivate student competency in politically aware data analysis and interpretation. I show how combined, the reading strategies prompt students to grapple with the double binds of perceiving contemporary problems through systems of representation that are always situated, incomplete, and inflected with diverse politics. While I introduce these methods in the context of teaching, I argue that the methods are integral to any data practice in the conclusion.

所有数据集都来自并融入充满力量的符号系统。虽然新兴的数据伦理课程支持数据科学学生识别数据偏见及其后果，但需要对文化历史和既得利益赋予数据语义的关键关注，以阐明数据所依赖的假设和政治承诺，以及它们产生的外部性. 在本文中，我介绍了研究数据集时可以采用的三种阅读模式——外延阅读（推断数据集中值的字面意义）、内涵阅读（追溯数据语义的社会政治出处）和解构阅读（通过数据语义和结构寻找什么可以得到其他）。然后，我概述了我如何教学生在分析数据与社会中的三个数据集时使用这些方法——这门课程旨在培养学生在具有政治意识的数据分析和解释方面的能力。我展示了这些阅读策略如何结合起来，促使学生通过始终处于、不完整且受不同政治影响的表征系统来应对感知当代问题的双重束缚。虽然我在教学中介绍了这些方法，但我认为这些方法对于结论中的任何数据实践都是不可或缺的。阅读策略促使学生通过始终处于、不完整且受不同政治影响的表征系统来应对感知当代问题的双重束缚。虽然我在教学中介绍了这些方法，但我认为这些方法对于结论中的任何数据实践都是不可或缺的。阅读策略促使学生通过始终处于、不完整且受不同政治影响的表征系统来应对感知当代问题的双重束缚。虽然我在教学中介绍了这些方法，但我认为这些方法对于结论中的任何数据实践都是不可或缺的。