Vincent Conitzer


Vincent Conitzer is Head of Technical AI Engagement at the Institute for Ethics in AI, and Professor of Computer Science and Philosophy, at the University of Oxford. He is also Professor of Computer Science (with affiliate/courtesy appointments in Machine Learning, Philosophy, and the Tepper School of Business) at Carnegie Mellon University, where he directs the Foundations of Cooperative AI Lab (FOCAL). Previous to joining CMU, he was the Kimberly J. Jenkins Distinguished University Professor of New Technologies and Professor of Computer Science, Professor of Economics, and Professor of Philosophy at Duke University. 

Conitzer has received the 2021 ACM/SIGAI Autonomous Agents Research Award, the Social Choice and Welfare Prize, a Presidential Early Career Award for Scientists and Engineers (PECASE), the IJCAI Computers and Thought Award, and an honorable mention for the ACM dissertation award. He has also been named a Guggenheim Fellow, a Sloan Fellow, a Kavli Fellow, a Bass Fellow, an ACM Fellow, a AAAI Fellow, and one of AI's Ten to Watch.


Research interests:

Much of Conitzer’s work has focused on AI and game theory, for example designing algorithms for the optimal strategic placement of defensive resources. More recently, he has started to work on AI and ethics: how should we determine the objectives that AI systems pursue, when these objectives have complex effects on various stakeholders?

Selected publications:

  • Vincent Conitzer, Gillian Hadfield, Shannon Vallor. 'Technical Perspective: The Impact of Auditing for Algorithmic Bias'. Communications of the ACM, Volume 66, Issue 1, January 2023, pp. 100
  • Steven Jecmen, Hanrui Zhang, Ryan Liu, Fei Fang, Vincent Conitzer, Nihar Shah. 'Near-Optimal Reviewer Splitting in Two-Phase Paper Reviewing and Conference Experiment Design'. Tenth AAAI Conference on Human Computation and Crowdsourcing (HCOMP-22), pages 102-113, 2022
  • Scott Emmons, Caspar Oestherheld, Andrew Critch, Vincent Conitzer, Stuart Russell. 'For Learning in Symmetric Teams, Local Optima are Global Nash Equilibria'. In Proceedings of the 39th International Conference on Machine Learning (ICML-22), pages 5924-5943, Baltimore, MD, USA, 2022
  • Hanrui Zhang, Yu Cheng, Vincent Conitzer. 'Efficient Algorithms for Planning with Participation Constraints'. In Proceedings of the 23rd ACM Conference on Economics and Computation (EC-22), pages 1121-1140, Boulder, CO, USA, 2022
  • Hanrui Zhang, Yu Cheng, Vincent Conitzer. 'Planning with Participation Constraints'. In Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-2022), pages 5260-5267, 2022

    A full list of publications can be found here


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