Ethics have become an urgent concern for data science research, practice, and instruction in the wake of growing critique of algorithms and systems showing that they reinforce structural oppression. There has been increasing desire on the part of data science educators to craft curricula that speak to these critiques, yet much ethics education remains individualized, focused on specific cases, or too abstract and unapplicable. We synthesized some of the most popular critical data science works and designed a data science ethics course that spoke to the social phenomena at the root of critical data studies -- theories of oppression, social systems, power, history, and change -- through analysis of a pressing sociotechnical system: surveillance systems. Through analysis of student reflections and final projects, we determined that at the conclusion of the semester, all students had developed critical analysis skills that allowed them to investigate surveillance systems of their own and identify their benefits, harms, main proponents, those who resist them, and their interplay with social systems, all while considering dimensions of race, class, gender, and more. We argue that this type of instruction -- directly teaching data science ethics alongside social theory -- is a crucial next step for the field.

翻译：伦理已成为数据科学研究、实践与教学中的紧迫议题，因为日益增多的算法与系统批判表明它们强化了结构性压迫。数据科学教育工作者越来越希望设计出能回应这些批判的课程，然而，多数伦理教育仍停留在个体化层面——要么聚焦于特定案例，要么过于抽象而缺乏实用性。我们综合了一些最受欢迎的批判性数据科学著作，设计了一门数据科学伦理课程，该课程通过分析一个紧迫的社会技术系统——监控系统——来探讨批判性数据研究根源处的社会现象：压迫理论、社会系统、权力、历史与变革。通过分析学生的反思与期末项目，我们发现，在学期结束时，所有学生都掌握了批判性分析技能，能够自主研究监控系统，识别其益处与危害、主要推动者、抵抗者及其与社会系统的互动关系，同时考虑到种族、阶级、性别等维度。我们认为，这种直接将数据科学伦理与社会理论相结合的教学方式，是该领域至关重要的下一步。