超越案例研究：通过社会技术监控研究教授数据科学批判与伦理
Beyond case studies: Teaching data science critique and ethics through sociotechnical surveillance studies
AI总结：
本文设计并评估一门以监控系统为切入点、融合社会理论的数据科学伦理课程，证明学生可形成系统性批判分析能力。
摘要：
随着对算法和系统强化结构性压迫的批判日益增多，伦理已成为数据科学研究、实践和教学中的紧迫关切。数据科学教育者越来越希望制定能回应这些批判的课程，但许多伦理教育仍然停留在个体化层面、聚焦特定案例，或者过于抽象且无法应用。我们综合了一些最具影响力的批判性数据科学著作，并设计了一门数据科学伦理课程，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和期末项目，我们确定在学期结束时，所有学生都发展出了批判性分析技能，使他们能够调查自己选择的监控系统，并识别其益处、危害、主要支持者、抵制者，以及它们与社会系统的相互作用，同时考虑种族、阶级、性别等维度。我们认为，这种教学方式——将数据科学伦理与社会理论直接结合起来教授——是该领域至关重要的下一步。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
