The Shortest Path to Ethics in AI: An Integrated Assignment Where Human Concerns Guide Technical Decisions

The Shortest Path to Ethics in AI: An Integrated Assignment Where Human Concerns Guide Technical Decisions
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人工智能道德的最短路径:人类关注指导技术决策的综合任务

DOI:
10.1145/3501385.3543978
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发表时间:
2022
期刊:
ICER '22: Proceedings of the 2022 ACM Conference on International Computing Education Research
影响因子:
--
通讯作者:
Wiese, Eliane S.
Wiese, Eliane S.
中科院分区:
--
文献类型:
--
作者:
Brown, Noelle;South, Koriann;Wiese, Eliane S.

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我们如何教AI学生使用人类关注的问题来指导他们的技术决策?我们创建了一个人工智能作业,要求学生找到最安全的路径,而不是最短的路径。这个综合作业评估了120名学生对标准图搜索算法的局限性和假设的理解,并要求学生考虑人类的影响,提出适当的修改。由于作业的重点是算法的选择和修改,它为教师提供了一个不同的视角来理解学生(与算法执行问题相比)。具体来说,很多学生:试图用为积累问题设计的算法来解决瓶颈问题,没有区分在路径的增量构建期间可以完成的计算与需要完整路径知识的计算,并且,当提出对标准算法的修改时,没有提出实现其想法所需的完整技术细节。我们设计了规则来分析学生的反应。我们的规则涵盖三个方面:技术人工智能知识,对人为因素的考虑,以及技术决策的整合,因为它们与人类环境保持一致。这些规则展示了学生的技能如何在沿着每个维度变化,也为其他CS主题的综合作业评分提供了模板。总的来说,这项工作展示了如何将人类的关注与技术内容相结合,以加深技术的严谨性,并支持教师教学内容的知识。
How can we teach AI students to use human concerns to guide their technical decisions? We created an AI assignment with a human context, asking students to find the safest path rather than the shortest path. This integrated assignment evaluated 120 students’ understanding of the limitations and assumptions of standard graph search algorithms, and required students to consider human impacts to propose appropriate modifications. Since the assignment focused on algorithm selection and modification, it provided the instructor with a different perspective on student understanding (compared with questions on algorithm execution). Specifically, many students: tried to solve a bottleneck problem with algorithms designed for accumulation problems, did not distinguish between calculations that could be done during the incremental construction of a path versus ones that required knowledge of the full path, and, when proposing modifications to a standard algorithm, did not present the full technical details necessary to implement their ideas. We created rubrics to analyze students’ responses. Our rubrics cover three dimensions: technical AI knowledge, consideration of human factors, and the integration of technical decisions as they align with the human context. These rubrics demonstrate how students’ skills can vary along each dimension, and also provide a template for scoring integrated assignments for other CS topics. Overall, this work demonstrates how to integrate human concerns with technical content in a way that deepens technical rigor and supports instructor pedagogical content knowledge.
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