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：随着对算法和系统的批判日益增多，揭示其强化结构性压迫的问题，伦理已成为数据科学研究、实践和教学中亟待关注的核心议题。数据科学教育者愈发渴望构建能够回应这些批判的课程体系，然而现有的伦理教育仍多局限于个体层面、聚焦特定案例，或过于抽象而难以应用。本研究综合了若干具有影响力的批判性数据科学著作，设计了一门数据科学伦理课程，该课程通过对紧迫性社会技术系统——监控系统——的分析，深入探讨批判性数据研究根源处的社会现象，包括压迫理论、社会制度、权力结构、历史脉络与变革机制。通过对学生反思报告与期末项目的分析，我们发现学期结束时，所有学生均掌握了批判性分析技能，能够自主调查各类监控系统，并系统辨识其效益、危害、主要推动者、抵制群体及其与社会制度的相互作用，同时全面考量种族、阶级、性别等多重维度。我们认为，这种将数据科学伦理与社会理论直接融合的教学模式，是该领域迈向下一发展阶段的关键步骤。