Get out of the BAG! Silos in AI Ethics Education: Unsupervised Topic Modeling Analysis of Global AI Curricula
Get out of the BAG! Silos in AI Ethics Education: Unsupervised Topic Modeling Analysis of Global AI Curricula
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从袋子里出来！
DOI：
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发表时间：
2022
影响因子：
5
通讯作者：
Junaid Qadir
中科院分区：
文献类型：
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作者：
R. T. Javed;Osama Nasir;Melania Borit;Loïs Vanhée;Elias Zea;Shivam Gupta;Ricardo Vinuesa;Junaid Qadir
人工智能（AI）伦理领域并不新鲜，讨论至少可以追溯到40年前。向学生教授道德AI的原则和要求被认为是这一领域的重要组成部分，地球仪的几个高等教育机构教授的技术AI课程越来越多，其中包括与道德相关的内容。通过使用潜在狄利克雷分配（LDA），一个生成概率主题模型，本研究揭示了AI课程中的伦理教学主题及其趋势，这些主题与课程的教学地点，由谁教授，以及根据布卢姆的分类法，认知复杂性和特异性的水平有关。在这项基于无监督机器学习的探索性研究中，我们共分析了166门课程：116门来自北美大学，11门来自亚洲，36门来自欧洲，10门来自其他地区。基于这种分析，我们能够综合教学方法的模型，我们称之为BAG（建立，评估和治理），它结合了特定的认知水平，课程内容主题和与负责该课程的部门相关的学科。我们批判性地评估这种教学模式的影响，并提供有关如何摆脱这些做法的建议。我们要求教学实践者和项目协调员反思他们的常规程序，以便他们能够超越陈规定型的思想和传统偏见的范围，扩大他们的方法论，关于什么学科应该教和如何教。
这篇文章出现在AI &社会轨道上。
The domain of Artificial Intelligence (AI) ethics is not new, with discussions going back at least 40 years. Teaching the principles and requirements of ethical AI to students is considered an essential part of this domain, with an increasing number of technical AI courses taught at several higher-education institutions around the globe including content related to ethics. By using Latent Dirichlet Allocation (LDA), a generative probabilistic topic model, this study uncovers topics in teaching ethics in AI courses and their trends related to where the courses are taught, by whom, and at what level of cognitive complexity and specificity according to Bloom’s taxonomy. In this exploratory study based on unsupervised machine learning, we analyzed a total of 166 courses: 116 from North American universities, 11 from Asia, 36 from Europe, and 10 from other regions. Based on this analysis, we were able to synthesize a model of teaching approaches, which we call BAG (Build, Assess, and Govern), that combines specific cognitive levels, course content topics, and disciplines affiliated with the department(s) in charge of the course. We critically assess the implications of this teaching paradigm and provide suggestions about how to move away from these practices. We challenge teaching practitioners and program coordinators to reflect on their usual procedures so that they may expand their methodology beyond the confines of stereotypical thought and traditional biases regarding what disciplines should teach and how.
This article appears in the AI & Society track.
影响因子：
22.7
作者：
Burton, Emanuelle;Goldsmith, Judy;Mattei, Nicholas
通讯作者：
Mattei, Nicholas