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S.J.D. Candidate

Berkman Klein Center Affiliate

mlevesque at



AI Governance: Towards a Polycentric Model

Artificial intelligence (AI) governance is currently siloed, duplicated and inefficiently allocated among stakeholders. The project aims to equip government actors with tools to fulfill their responsibilities as stewards of the public good in the AI space. A polycentric model posits AI governance as a spiderweb, with states occupying a central role and creatively arbitrating among a plurality of interrelated interests.

Fields of Research and Supervisors

  • Legal aspects of governance and AI-driven discrimination with Professor Martha Minow, Harvard Law School, Principal Faculty Supervisor
  • Legal theory and governance in the technology sector with Professor Lawrence Lessig, Harvard Law School
  • Organizational theory and emerging perspectives on labor law with Professor Ifeoma Ajunwa, Cornell Law School

Additional Research Interests

  • Labor Law and #TechWontBuildIt Movement
  • Machine Learning Explainability and Interpretability
  • Privacy Law and Surveillance Studies
  • Corporate Social Responsibility and Human Rights Law


  • Harvard Law School, S.J.D. Candidate 2020 – Present
  • Harvard Law School, LL.M. Program 2019-2020 (requirements fulfilled, degree waived)
  • McGill University, Canada, B.C.L./ LL.B. 2012
  • Concordia University, Canada, B.F.A. Computation Arts 2007

Representative Publications

  • NeurIPS, Regulatory frameworks relating to data privacy and algorithmic decision-making in the context of algorithmic bias, Montreal, 2018
  • Stem Cell Reviews and Reports, Stem Cell Research Funding Policies and Dynamic ­­­­Innovation: A Survey of Open Access and Commercialization Requirements, Springer, New York, 2014
  • Inter-Society for the Electronic Arts, Lost in Transportation: Passage Oublié, Singapore, 2008

Additional Information

  • IEEE (Institute of Electrical and Electronics Engineers), Algorithmic Bias Working Group

Last Updated: October 20, 2020