抽象有政治吗？迈向更关键的算法分析
Do Abstractions Have Politics? Toward a More Critical Algorithm Analysis
摘要：
在K-12和高等教育在美国的计算机科学（CS）教育的扩展，促使更深入的参与与公平，超越包容走向更关键的CS教育。而不是框架计算作为一个价值中立的工具，以公正为中心的方法来公平CS教育借鉴批判教学法，以确保政治斗争的合法存在，强调不仅知识和技能的发展，但也CS学科身份。虽然最近的努力已经整合到本科CS课程的几个领域的道德，关键的教学方法数据结构和算法，特别是undertheorized。基本数据结构仍然集中于以运行时为中心的算法分析。我们主张启示分析，一个更关键的算法分析的基础上的启示帐户的价值嵌入。借鉴科学技术研究、技术哲学和人机交互的批判方法，启示分析研究了计算抽象（如数据结构和算法）的设计如何体现启示，而启示又体现了具有政治后果的价值。我们展示了5个案例研究，说明了可供性分析如何反驳技术的社会决定，突出了数据抽象的局限性，并暗示了算法的设计在统治矩阵中不成比例地分配利益和伤害特定的社会身份。
英文摘要：
The expansion of computer science (CS) education in K--12 and higher-education in the United States has prompted deeper engagement with equity that moves beyond inclusion toward a more critical CS education. Rather than frame computing as a value-neutral tool, a justice-centered approach to equitable CS education draws on critical pedagogy to ensure the rightful presence of political struggles by emphasizing the development of not only knowledge and skills but also CS disciplinary identities. While recent efforts have integrated ethics into several areas of the undergraduate CS curriculum, critical approaches for teaching data structures and algorithms in particular are undertheorized. Basic Data Structures remains focused on runtime-centered algorithm analysis. We argue for affordance analysis, a more critical algorithm analysis based on an affordance account of value embedding. Drawing on critical methods from science and technology studies, philosophy of technology, and human-computer interaction, affordance analysis examines how the design of computational abstractions such as data structures and algorithms embody affordances, which in turn embody values with political consequences. We illustrate 5 case studies of how affordance analysis refutes social determination of technology, foregrounds the limitations of data abstractions, and implicates the design of algorithms in disproportionately distributing benefits and harms to particular social identities within the matrix of domination.
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