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 report about their experiences. 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, which resulted in 9 reports from our course being accepted to the challenge. We reflect on our experience teaching the course over two academic years, where one year coincided with a global pandemic, and propose guidelines for teaching FACT-AI through reproducibility in graduate-level AI programs. We hope this can be a useful resource for instructors to set up similar courses at their universities 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、保密和人工情报透明问题的技术、研究生级课程(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小组项目,其基础是复制自AI最高级会议现有的FACT-AI算法,并撰写关于其经验的报告。在课程的第一次迭代中,我们建立了一个开放源码库,该课程的代码执行来自该组项目。在第二个迭代中,我们鼓励学生将其集体项目提交给机器学习再生挑战,导致我们课程的9份报告被接受到挑战。我们反思了我们两个学年的课程教学经验,其中一年与全球大流行病同时,我们提出了通过在研究生一级AI方案中重新教授FACT-AI的指导方针。我们希望这可以成为教员今后在大学开设类似课程的有用资源。