An interdisciplinary team
Our interdisciplinary team federates six academic fields (applied math, statistics, computer science, economics, law and sociology) around the ethical issues raised by artificial intelligence.
Five research pillars
Algorithmic bias and fairness
AI and General Interest
Governance and regulation
The production of data from networks, connected objects, sensors or industrial processes has largely contributed to the digital transition of companies, which now consider data as a resource in its own right. All of a company’s activities and jobs have now been impacted, resulting in a growing need for training for teams and managers. Télécom Paris offers a variety of training options tailored to meet all needs.
Specialized Studies Certificates
Télécom Évolution offers two specialized studies certificates, Data Scientist and Artificial Intelligence, both of which are delivered by Télécom Paris, with contributions from ENSTA Paris for the second certificate. In collaboration with École polytechnique d’Assurance, it offers an Executive MBA Data Scientist for Insurance Careers.
Télécom Évolution also offers many inter-company training courses as well as the MOOC, The Basics of Big Data, which enrolls 7,000 learners every session.
Télécom Evolution offers the short training course The ethical challenges of artificial intelligence. This 2-day training course provides an overview of the main ethical, legal and societal issues surrounding AI.
Interpretability, Accountability and Robustness in Machine LearningDigital Trust, Data Science & AI — 16/02/2021Nowadays, data science, machine learning, artificial intelligence based solutions being integrated in [...]
Federated learning: the privacy-friendly artificial intelligence?Digital Trust, Data Science & AI — 18/01/2021Federated learning is a new concept emerged from Google Labs. The aim is to make a computer learn [...]
Columbia University “Machine Learning in Science & Engineering” Conference— 08/12/2020Florence d’Alché-Buc, Professor at Télécom Paris, Institut Polytechnique de Paris, will give a keynote [...]
Do machines understand what they read?Digital Trust, Data Science & AI — 22/10/2020One of the challenges of natural language processing (NLP) is to get the meaning right. Subtle changes in a sentence can [...]
Explainability and fundamental rightsDigital Trust, Data Science & AI, Faculty Members — 16/10/2020When does the law require an algorithmic decision to be explained? A number of laws (GDPR, competition law, [...]
Four positions opened to work on extracting complex information from natural language textData Science & AI — 02/10/2020We are looking for two PhD, one Postdoc, and one Research Engineer.
To simplify AI regulation, use the GDPR’s high-risk criteriaDigital Trust, Data Science & AI, Faculty Members — 22/09/2020There are a multitude of contexts where we would expect an AI application to be [...]
Facebook: German Supreme Court reopens the way for data regulation under competition lawDigital Trust, Data Science & AI, Faculty Members — 10/09/2020The "Facebook" saga in Germany has a new twist after the [...]
The ACPR’s guidelines on explainability: clarifications and ambiguitiesDigital Trust, Data Science & AI — 28/08/2020Machine learning can be of great help in the fight against money laundering by helping to [...]
Why facial recognition algorithms can’t be perfectly fairDigital Trust, Faculty Members — 20/07/2020By W. Maxwell and S. Clémençon. Facial recognition algorithms have been shown to be less accurate for [...]
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