Nicholas Vincent headshot

Nicholas Vincent

Assistant Professor · School of Computing Science, Simon Fraser University

Human activity is upstream of (many) AI capabilities; any system with data dependence creates potential governance and bargaining power; this has major implications for AI safety and for understanding how AI allocates power and delivers value to the world.

Short bio

Prof. Nick Vincent is an Assistant Professor in Computing Science at Simon Fraser University and directs the People- and Data-centric Computing Research Group (PadComp). He studies the content ecosystems and data supply chains that fuel data-dependent technologies like search engines, recommender systems, and generative AI. This involves exploring avenues for people to control how data flows and participate in the governance of AI systems. The overarching goal of this research is to work towards highly capable and widely beneficial AI technologies that mitigate—rather than exacerbate—inequalities in wealth and power. His dissertation received the 2024 ACM SIGCHI Outstanding Dissertation Award.

Long bio
Nicholas Vincent headshot
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Nick Vincent is an Assistant Professor in Computing Science at Simon Fraser University and director of the People- and Data-centric Computing Research Group (PadComp). He studies the content ecosystems and data supply chains that fuel data-dependent technologies like search engines, recommender systems, and generative AI. This involves exploring avenues for people to control how data flows and participate in the governance of AI systems. The overarching goal of this research is to work towards highly capable and widely beneficial AI technologies that mitigate—rather than exacerbate—inequalities in wealth and power.

His work is published in responsible AI venues like ACM FAccT and human-computer interaction venues like ACM CHI. His dissertation on data leverage received the 2024 ACM SIGCHI Outstanding Dissertation Award.

Prof. Vincent was previously a postdoc working with the Computational Communication Research Lab at UC Davis and the Social Futures Lab at the University of Washington. He received his PhD from Northwestern University's Technology and Social Behavior program (a joint degree in computer science and communication), where he worked in the People, Space, and Algorithms Research Group.

During graduate school, he was a research intern at Snap and Microsoft. Before graduate school, he studied electrical engineering at UCLA, interned at Cisco, and conducted research at SPAWAR via the NREIP program.

Publications

Peer Reviewed

Workshop / Pre-print

Other Writing

Talks & Conversations

Media Coverage

Web Projects

  • DataLicenses.org

    Live

    A community-curated catalog of initiatives shaping AI data access, licensing, controlled retrieval, and enforcement.

    AI data licensingpreference signalsdata access

    VisitSep 2025
  • Data Counterfactuals

    Live

    Interactive memos, toy grids, and related-work maps for reasoning about what changes when training, evaluation, or governance data worlds change.

    data valuationevaluationdata leverage

    VisitMar 2026
  • Data Napkin Math

    Live

    A beta interactive page for exploring back-of-the-envelope estimates about training data value and the distribution of AI proceeds and benefits.

    data valueinteractive estimatesAI economics

    VisitJan 2024
  • Data Lava Lamp

    Live

    An interactive AI-literacy installation that turns fragments of open training and post-training datasets into drifting streams of text.

    AI literacytraining datainteractive installation

    VisitJul 2026

Workshops & Meetings

Workshops, conferences, and recurring meetings I have organized, facilitated, or supported.

What this site is for

This site is a searchable companion to my CV: a list of papers, talks, writing, workshops, meetings, and other research artifacts, with filters and the links attached to each project. Like some other academics, I occasionally use my own static site to find a paper link before sending it to someone. I also group the work into 9 research topics, though they are really different angles on data leverage.