Short bio
CNRS senior scientist (2008-), LAMSADE (CNRS, PSL, Université Paris-Dauphine). CNRS silver medal 2017. EurAI fellow since 2009. Associate editor of Journal of Artificial Intelligence Research (2009-2015), Artificial Intelligence Journal (2010-2016), Social Choice and Welfare (2016-). Program chair of IJCAIECAI-2018, General chair of ECAI-2020. Recipient of the Humboldt Research Award 2021.
Topics of interest
Computational social choice, algorithmic game theory, fairness, algorithmic decision theory, knowledge representation
Project in Prairie
Jérôme Lang’s research project focuses on the use of social-choice theoretic, decision-theoretic, and game-theoretic principles and tools for AI. This goes in two opposite directions: from economics to AI, and from AI to economics. He will especially focus in domains where AI helps taking decisions for groups of people. He plans to work on social-choice principles for ethics and fairness in AI, and more generally for assessing the social acceptability of AI algorithms.
Quote
Normative criteria developed in economics (such as fairness, equity, anonymity, privacy, strategyproofness, efficiency), can be help defining frameworks for analysing the ethics and social acceptability of AI algorithms, and investigating the trade-offs that have to be made between incompatible criteria. Notions of fairness in AI and social choice are slightly different but convergent, and the various notions of fairness studied in social choice are relevant to AI research. Moreover, AI techniques are useful for making collective decisions: especially, ML methods for learning and eliciting users’ preferences help making collective decisions that offer a good trade-off between the quality of the outcome and the communication burden. This applies to various areas of public decision making, such as allocation problems (matching students to universities, organs to patients, designing fair and robust schedules in hospitals or high schools), or fair and efficient public spending.
Team
