Raja Chatila

Chair - IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems

Working groupExpert Group on AI Risk & Accountability

Working groupExpert Group on AI Incidents

Stakeholder TypeTechnical community

GPAI

ONE AI Observer

AI Wonk contributor

Raja Chatila is Professor Emeritus of Artificial Intelligence, Robotics and IT Ethics at Sorbonne University in Paris. He is former director of the SMART Laboratory of Excellence on Human-Machine Interactions and of the Institute of Intelligent Systems and Robotics. He is an IEEE Fellow and recipient of the IEEE Robotics and Automation Society Pioneer Award. He is co-chair of the Responsible AI Working group of the Global Partnership in AI and was co-chair of the World Economic Forum Global Future Council on AI for Humanity. He is chair of the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems.

Raja Chatila's publications

Intergovernmental

A responsible AI strategy for climate action

Recommendations for government action ahead of COP26 from the Centre for AI and Climate Change, Global Partnership on Artificial Intelligence, and Climate Change AI

October 30, 2021 — 5 min read

Intergovernmental

GPAI’s Responsible AI Working Group work in 2021

Responsible AI strategy for the environment and for social media governance are this working group’s priorities.

June 3, 2021 — 2 min read

Multistakeholder

An introduction to the Global Partnership on AI’s work on Responsible AI

The Global Partnership on AI (GPAI) has a mission to “support the development and use of AI based on human rights, inclusion, diversity, innovation, and economic growth, while seeking to address the U...

September 1, 2020 — 6 min read

Raja Chatila's videos

Developing and applying AI: Core and non-core AI

February 6, 2021–

60 mins

AI technologies are still in their relative infancy but already promise to have a strong effect on production and, consequently, jobs. To help understand the state of the art and the directions AI will take, this session will discuss how to distinguish core versus non-core (i.e. applications) AI-related developments. It will further discuss experimental evidence based on web-reading at scale about the economic agents (i.e. firms, universities, etc.) that are active in the AI space, as well as research using trademark data to shed light on firms developing and positioning AI-related goods and services in the marketplace. Panellists will discuss ongoing approaches and help explore ways forward to deepen our understanding.

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