Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence through the Lens of Reproducibility

• URL: http://arxiv.org/abs/2111.00826v2
• Date: Tue, 2 Nov 2021 13:06:21 GMT
• Title: Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence through the Lens of Reproducibility
• Authors: Ana Lucic, Maurits Bleeker, Sami Jullien, Samarth Bhargav, Maarten de Rijke
• Abstract summary: 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. The focal point of the course is a group project based on existing FACT-AI algorithms from top AI conferences, and writing a report about their experiences.
• Score: 38.87910190291545
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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