In this work, we explain the setup for a technical, graduate-level course on Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence (FACT-AI) at the University of Amsterdam, which teaches FACT-AI concepts through the lens of reproducibility. The focal point of the course is a group project based on reproducing existing FACT-AI algorithms from top AI conferences and writing a corresponding report. In the first iteration of the course, we created an open source repository with the code implementations from the group projects. In the second iteration, we encouraged students to submit their group projects to the Machine Learning Reproducibility Challenge, resulting in 9 reports from our course being accepted for publication in the ReScience journal. We reflect on our experience teaching the course over two years, where one year coincided with a global pandemic, and propose guidelines for teaching FACT-AI through reproducibility in graduate-level AI study programs. We hope this can be a useful resource for instructors who want to set up similar courses in the future.
翻译:在这项工作中,我们解释了在阿姆斯特丹大学开设关于公平、问责、保密和人工智能透明度的研究生级技术课程(FACT-AI)的设置,该课程从可复制的角度讲授FACT-AI概念;该课程的协调中心是一个集体项目,其基础是复制自AI最高级会议现有的FACT-AI算法,并撰写相应的报告;在课程的第一次迭代中,我们创建了一个开放源码库,其中含有小组项目的代码执行;在第二个迭代中,我们鼓励学生向机器学习再生挑战提交他们的集体项目,导致我们课程的9份报告被接受在Recience杂志上发表;我们反思了我们两年来讲授的课程的经验,其中一年与全球大流行病同时发生,并提出了通过在研究生一级AI研究方案中重新教授FACT-AI的指导方针;我们希望这可以成为今后希望开设类似课程的教员的有用资源。