Data Scientist & AI Ethics
Data Scientist and coordinator of the academic chair Good In tech (Institut Mines-Télécom / Sciences Po), my research activities are in the field of technology ethics and more particularly of artificial intelligence (AI Ethics). I am a teacher at Sciences Po on Algorithms & Public Policies and at PSL University in Mathematics and Probability.
In the past, I worked as a Data Scientist and AI engineer after graduating in Mathematics (Mines and ENSAE), Economics (Sciences Po / Polytechnique and PSE) and Philosophy (Sorbonne and ICP).
I work on the ethical issues created by the implementation of artificial intelligence. My research questions are as follows:
- How to root AI ethics in existing philosophical theories?
- What are the formal metrics for measuring the biases and discriminations induced by artificial intelligence? What are their limits ?
- How can we experimentally measure the impacts of so-called ethical tools in AI?
- How to consider the ethical expectations expressed by citizens on AI to integrate them in the design of algorithms?
Data Science & AI
My master thesis at ENSAE focused on distributed Machine Learning and the application of the stochastic gradient descent method on distributed file (Hadoop / HDFS). You will find on my Github account my various algorithmic developments as a Data Scientist, in particular the implementation of AI fairness metrics applied to credit scoring, graph mining, topic modeling, blog scraping, etc.
I was interested inepistemological issues around artificial intelligence as part of my Master thesis in philosophy. My work has focused on:
- The phenomenological arguments put forward by Dreyfus against the possibility of a formal AI;
- The epistemological foundations of interceptability in Machine Learning;
- The historical epistemology of the discipline of artificial intelligence.
Economy & public policy
I worked in econometrics on the correction of endogeneity in high-dimensional context, and in particular on the following subjects:
- Estimation of wage equations when the number of instrumental variables are large;
- The simulation of the behavior of the STIV (Self Tuning Instrumental Variables) estimator by Gautier, Tsybakov (2012)
Political science :
- Algorithms & Public Policies, M2, Master in Public Policies, School of Public Affairs:
2019/2020 and 2020/2021
- Quantitative methods for the social sciences, L1, University College:
- Analysis and evaluation of public policies, M2, Master of Public Policies, School of Public Affairs:
Paris Sciences & Lettres University (PSL):
- Mathematics & Probability, Multidisciplinary Cycle of Higher Studies (CPES), 2nd year:
2016/2017, 2017/2018, 2018/2019 and 2019/2020
- Statistics, Multidisciplinary Cycle of Higher Studies (CPES), 2nd year:
2016/2017, 2017/2018, 2018/2019 and 2019/2020
Paris Sorbonne University:
- Econometrics, L3, Bachelor of Economics:
- Statistics, L3, Bachelor of Economics:
Conference: "At the heart of algorithms. Scientific approach to automation, ethical, legal and societal issues"
IImagine Institute, Ethics Space, February 6, 2020, Jean-Marie John-Mathews
Video conference link
Round Table videolink
Conference: "why is AI a problem?"
Metz University of Law and Economics, January 17, 2020, Jean-Marie John-Mathews
Summary : In recent years, many applications such as autonomous cars, virtual assistants, recommendation systems, facial or voice recognition use Machine Learning, a sub-discipline of artificial intelligence (AI). These uses are the subject of debate and today pose many ethical problems of a varied nature such as acts of discrimination, infringement of individual freedoms, the autonomy of subjects or even the responsibility of algorithms given their opacity. In this lecture, we will present these different concerns from a technical point of view. A growing community of researchers and engineers in artificial intelligence are dealing with this ethical subject by offering so-called responsible tools in AI. From the presentation of these so-called responsible tools and methods, we will expose their theoretical assumptions as well as their limits and contradictions.
Conference: "The mathematical engineer in a world of artificial intelligence"
Paris Diderot University, February 18, 2019, Jean-Marie John-Mathews
Summary : In recent years, many applications such as autonomous cars, virtual assistants, recommendation systems, facial or voice recognition claim to "use artificial intelligence". What does that mean ? A more precise vision of the models within these technologies makes it possible to identify their common denominator which is none other than a discipline within applied mathematics: machine learning. What place for the mathematical engineer in a world of artificial intelligence? Has his role in society changed? We will see in this conference how the design of algorithms put the engineer at the heart of the ethical and societal issues induced by artificial intelligence.