Master MVA / Responsible Machine Learning

2025/2026

*** Syllabus

Machine Learning is a fantastic scientific field but it is also involved in a (geo)political arena. And it may seriously harm individuals or societies.

The course tackles the three main issues of the implementation of Machine Learning from an ethical perspective: privacy, fairness, and explainability.

We introduce the main concepts and offer an overview of how such ethical considerations may be taken into account from a technical perspective.

***Instructors:
Aurélien BELLET

Nicolas VAYATIS

***Contact email :
<nicolas/-dot-/vayatis/-at-/ens-paris-saclay.fr>

***Course schedule and location:

Mondays afternoon at Université Paris-Cité / Campus Saint-Germain-des-Prés

Room Vieussens C (7th floor)

Date	Room	Session	Topics	Material
Monday November 17	Vieussens	Lecture #1	Introducing responsible ML
Monday November 24	Vieussens	Lecture #2	Machine Learning Fairness
Monday December 1	Vieussens	Lecture #3	Explainability in Machine Learning
Monday December 8	Vieussens	Lecture #4	TBD
Monday December 15	Vieussens	Lecture #5	Privacy-preserving Machine Learning
Monday January	Vieussens	Exam

***Evaluation:

G1 = grade of the one-hour exam

G2 = grade of the ten-minute presentation

Final grade = (G1+G2)/2

***Readings

**On privacy

*Dwork, Roth (2014): <https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf>

*Rigaki, Garcia (2023): <https://dl.acm.org/doi/pdf/10.1145/3624010>

*Kairouz et al. (2020): <https://arxiv.org/pdf/1912.04977>

**On fairness

Kleinberg, Mullainathan, Raghavan (2017): <https://doi.org/10.4230/LIPIcs.ITCS.2017.43>

Hardt, Price, Srebro (2016): <https://proceedings.neurips.cc/paper_files/paper/2016/file/9d2682367c3935defcb1f9e247a97c0d-Paper.pdf>

Mitchell et al. (2021): <https://cs.uwaterloo.ca/~jhoey/teaching/cogsci600/papers/Mitchell2021.pdf>

Domini et al. (2018): <https://arxiv.org/pdf/1802.08626>

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