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This website gathers the blogposts created in the Recent Advances in Responsible AI course. This course is part of the Master 2 Data Science program from IP Paris.

Artificial intelligence, as a transversal discipline, plays a central role in our modern society, driving vital advances and amplifying efficiency, well-informed decision-making and general practicality in our daily routines. This advanced master’s course aims to provide students with a comprehensive understanding of the latest developments in Responsible AI. The course will explore various facets of Responsible AI, including interpretable AI, fairness in machine learning, robust machine learning, data privacy, and frugality. Students will delve into both theoretical foundations and practical implementations, equipping them with the skills to design and implement AI systems that are ethical, accountable, and aligned with societal values.

Coordinators of the course

For questions or specific inquiries related to this course, please get in touch with Florence d’Alché-Buc and Charlotte Laclau

Organisation of the course

• Interpretable AI - Florence d’Alché-Buc
• Fairness in Machine Learning - Stephan Clemençon and Charlotte Laclau
• Robust Machine Learning - Quentin Bouniot
• Frugal AI - Enzo Tartaglione and Florence d’Alché-Buc
• Data Privacy - Yann Issartel
• Project supervision

Student Requirements

For this course, you will have to write a blog post on a scientific article assigned to you (to pick from the list presented here. This work can be done in teams (max 2 students). All posts will be published on this website. We also provide a tutorial that explain how to work and publish your final article.

• Examples of scientific blogposts can be found here: https://iclr-blogposts.github.io/2023/about

• In addition, you are requested to run and try to reproduce at least one of the experiments of the paper to write a reproducibility section, highlighting potential difficulties or missing information in your post.

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