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In this work, we explain the setup for a technical, graduate-level course onFairness, Accountability, Confidentiality, and Transparency in ArtificialIntelligence (FACT-AI) at the University of Amsterdam, which teaches FACT-AIconcepts through the lens of reproducibility. The focal point of the course isa group project based on reproducing existing FACT-AI algorithms from top AIconferences and writing a corresponding report. In the first iteration of thecourse, we created an open source repository with the code implementations fromthe group projects. In the second iteration, we encouraged students to submittheir group projects to the Machine Learning Reproducibility Challenge,resulting in 9 reports from our course being accepted for publication in theReScience journal. We reflect on our experience teaching the course over twoyears, where one year coincided with a global pandemic, and propose guidelinesfor teaching FACT-AI through reproducibility in graduate-level AI studyprograms. We hope this can be a useful resource for instructors who want to setup similar courses in the future.

Reproducibility as a Mechanism for Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence