Nick Vincent

Assistant professor of Computing Science at Simon Fraser University in British Columbia.


Research interests: Responsible Artificial Intelligence, Human-computer Interaction, Human-centered Machine Learning, and Social Computing.

More Info

CV as PDF

Data Leverage Blog

Semantic Scholar page

LinkedIn

Github

Contact

nickvincent@u.northwestern.edu

Twitter

hci.social


My research focuses on studying the relationship between human-generated data and modern computing technologies, including systems often referred to as "AI". The overarching goal of this research agenda is to work towards an ecosystem of widely beneficial, highly capable AI technologies that mitigate inequalities in wealth and power rather than exacerbating them. I believe working to make people aware of the value of their data contributions can help achieve this goal. My work relates to concepts such as "data dignity", "data as labor", "data leverage", and "data dividends".


I was previously a postdoc working with the Computational Communication Research Lab at UC Davis and the Social Futures Lab at the University of Washington. I received my PhD from Northwestern University's Technology and Social Behavior program (a joint degree in computer science and communication), where I worked in the People, Space, and Algorithms Research Group. During graduate school, I was a research intern at Snap and Microsoft. Before graduate school, I studied electrical engineering at UCLA.

News Coverage

  1. Value of Wikipedia in New York Times Magazine.

    Gertner, J. July 2023. Wikipedia’s Moment of Truth.

  2. Data labor in MIT Technology Review.

    Heikkilä, M. June 2023. We are all AI’s free data workers.

  3. Data poisoning in Le Monde.

    Defer, A. April 2022. Internet users are 'poisoning' their personal data in the fight against online surveillance.

  4. Wikipedia and search engines in Vox.

    Heilweil, R. June 2021. Got the same name as a serial killer? Google might think you’re the same person.

  5. Data dividends in Bloomberg.

    Coy, P. May 2021. Facebook and Others Should Pay Us for Our Data. Here’s One Way.

  6. Data leverage in the MIT Technology Review.

    Hao, K. March 2021. How to poison the data that Big Tech uses to surveil you.

  7. Data leverage in Fortune.

    Vanian, J. and Kahn, J. February 2021 Your data is a weapon that can help change corporate behavior. Also covered in the ACM TechNews.

  8. Data strikes in Quartz.

    Rivero, N. July 2020. Is it time for Netflix subscribers to go on strike?

  9. Value of Wikipedia in the New York Times.

    Herrman, J. March 2018. YouTube May Add to the Burdens of Humble Wikipedia. Also covered on the Northwestern Computer Science website.

Blog Posts and Op-eds

  1. A roadmap toward empowering the labor force behind AI. July 2023. Montreal AI Ethics Institute Research Summaries, co-authored with Hanlin Li, Brent Hecht, and Stevie Chancellor.
  2. ChatGPT Stole Your Work. So What Are You Going to Do? January 2023. Op-ed for Wired, co-authored with Hanlin Li.
  3. AI Technologies are System Maps, and You are a Cartographer. February 2023. Data Leverage Blog.
  4. AI Artist or AI Art Thief? Innovation, Public Mandates, and the Case for Talking in Terms of Leverage. December 2022. Data Leverage Blog.
  5. ChatGPT is Awesome and Scary: You Deserve Credit for the Good Parts (and Might Help Fix the Bad Parts). December 2022. Data Leverage Blog.
  6. The Paradox of Reuse, Language Models Edition. December 2022. Data Leverage Blog.
  7. GitHub Copilot and the Exploitation of “Data Labor”: A Wake-Up Call for the Tech Industry. July 2021. People, Space, and Algorithms Research Group Blog.
  8. Apple Now Lets You Opt-Out of Tracking: Will This Give You More Influence Over Tech Companies? July 2021. Guest post for Technically Social blog.
  9. A Casual Introduction to Public Goods and Collective Action Problems. Guest post for the Govrnance Substack. June 2021.
  10. Powerful Technologies and Their Power Laws: Estimating Machine Learning Systems' Data Leverage Vulnerabilities. Last Updated April 2021. Interactive Observable notebook post.
  11. What if we could check Big Tech?: The collective voice of millions of users could be as effective as regulation March 2021. Northwestern Now, co-authored with Hanlin Li.
  12. Why You’re an Expert "Language Model Trainer"! March 2021. People, Space, and Algorithms Research Group Blog.
  13. Don’t give OpenAI all the credit for GPT-3: You might have helped create the latest “astonishing” advance in AI too. September 2020. People, Space, and Algorithms Research Group Blog.
  14. "Data Strikes": A New Form of Leverage for Tech Users? Guest post on the Data Dividend Project blog. September 2020.

Multimedia

  1. (Video) Does the rise of AI need us to adopt new data licensing policies? December 2022.
  2. (Podcast) Should Tech Companies Be Paying Us for Our Data? - Things Have Changed Podcast . November 2021.
  3. (Podcast) On Data Dividends - RadicalxChange Podcast. With Yakov Feygin and Matt Prewitt, May 2021.
  4. (Video) Data Agency: Individual or Shared? With Matt Prewitt, Kaliya Young, and Jennifer Lyn Morone, Jan 2021.
  5. (Video) Data Driven Economy for All - 2020 RxC Conference. With Hanlin Li, Yakov Feygin, and Brent Hecht. July 2020.

Peer-reviewed Publications

  1. [FaccT 2023]

    Li, H., Vincent, N., Chancellor, S., and Hecht, B. 2023.

    The Dimensions of Data Labor: A roadmap for Activists, Researchers, and Practitioners to Empower Data Producers. In ACM FAcct 2023.

  2. [FaccT 2022]

    Contractor, D., McDuff, D., Haines, J., Lee, J., Hines, C., Hecht, B., Vincent, N., and Li, H. 2021.

    Behavioral Use Licensing for Responsible AI. In ACM FAcct 2022.

    Link to ACM DL

  3. [NeurIPS 2021 Datasets and Benchmarks Track]

    Bandy, J., and Vincent, N. 2021.

    Addressing 'Documentation Debt' in Machine Learning Research: A Retrospective Datasheet for BookCorpus. In NeurIPS 2021 Dataset Track.

    Link to arXiv

  4. [ICWSM 2021]

    Chowdhury, F.A., Liu, Y., Saha, K., Vincent, N. , Neves, L., Shah, N., and Bos, M.W. 2021.

    CEAM: The Effectiveness of Cyclic and Ephemeral Attention Models of User Behavior on Social Platforms. In Proceedings of the 15th International AAAI Conference on Web and Social Media (ICWSM).

  5. [CSCW 2021]

    Vincent. N. and Hecht, B. 2021.

    A Deeper Investigation of the Importance of Wikipedia Links to Search Engine Results. In CSCW 2021 / PACM Computer-Supported Cooperative Work and Social Computing.

  6. [CSCW 2021]

    Vincent. N. and Hecht, B. 2021.

    Can “Conscious Data Contribution” Help Users to Exert “Data Leverage” Against Technology Companies? In CSCW 2021 / PACM Computer-Supported Cooperative Work and Social Computing.

  7. [CHI 2021]

    Saha, K., Liu, Y., Vincent, N. , Chowdhury, F.A., Neves, L., Shah, N., and Bos, M. 2021.

    AdverTiming Matters: Examining User Ad Consumption for Effective Ad Allocations on Social Media. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems.

    Link to PDF Preprint

  8. [FAccT 2021]

    Vincent, N. , Li, H., Tilly, N., Chancellor, S., Hecht, B. 2021.

    Data Leverage: A Framework for Empowering the Public in its Relationship with Technology Companies. In Proceedings of the 2021 Conference on Fairness, Accountability, and Transparency.

  9. [CSCW 2019]

    Li, H. *, Vincent, N. *, Tsai, J., Kaye, J., and Hecht, B. 2019.

    How Do People Change Their Technology Use in Protest?: Understanding “Protest Users”. CSCW 2019 / PACM Computer-Supported Cooperative Work and Social Computing. * indicates equal contributions.

  10. [ICWSM 2019]

    Vincent, N., Johnson, I., Sheehan, P., and Hecht, B. 2019.

    Measuring the Importance of User-Generated Content to Search Engines. In AAAI ICWSM 2019.

  11. [The Web Conference 2019]

    Vincent, N., Hecht, B., and Sen, S. 2019.

    “Data Strikes”: Evaluating the Effectiveness of New Forms of Collective Action Against Technology Platforms. In The World Wide Web Conference (WWW '19).

    Link to ACM Digital Library (includes HTML version) | Link to PDF Preprint | Link to Archived code

  12. [CSCW 2018]

    Foong, E., Vincent, N., Hecht, B., and Gerber, E. 2018.

    Women (Still) Ask For Less: Gender Differences in Hourly Rate in an Online Labor Marketplace. ACM CSCW 2018.

  13. [CHI 2018]

    Vincent, N., Johnson, I., and Hecht, B. 2018.

    Examining Wikipedia with a Broader Lens: Quantifying the Value of Wikipedia's Relationships with Other Large-Scale Online Communities. In ACM CHI 2018.

    * Best paper award (top 1% of submissions)

Workshop Papers and Other Preprints

  1. [CESC 2022 (non-archival)]

    Vincent, N. and Vandevoorde, C.

    Collaborative Design of Contribution Tracking Systems for Decentralized Organizations. In CESC 2022.

  2. [CSCW 2022 Posters]

    Jones, I., Hecht, B., and Vincent, N.

    Misleading Tweets and Helpful Notes: Investigating Data Labor by Twitter Birdwatch Users. In CSCW 2022 Poster Track.

  3. [arXiv Preprint]

    Abhari, R., Vincent, N., Dambanemuya, H.K., Bodon, H., Horvát, E-A.

    Twitter Engagement with Retracted Articles: Who, When, and How? arXiv preprint arXiv:2203.04228

    Link to arXiv

  4. [WikiWorkshop 2020 (non-archival) ]

    Vincent, N. and Hecht, B. 2020.

    A Deeper Investigation of the Importance of Wikipedia Links to the Success of Search Engines. In WikiWorkshop 2020.

  5. [arXiv Preprint]

    Vincent, N., Li, Y., Zha, R. and Hecht, B., 2019.

    Mapping the Potential and Pitfalls of "Data Dividends" as a Means of Sharing the Profits of Artificial Intelligence. arXiv preprint arXiv:1912.00757.

    Link to arXiv

  6. [BIBM 2015 Workshop on Biomedical Visual Search and Deep Learning]

    Stier, N., Vincent, N. , Liebeskind, D. and Scalzo, F. 2015.

    Deep learning of tissue fate features in acute ischemic stroke. In Bioinformatics and Biomedicine (BIBM) 2015.

    Link to IEEE Explore

  7. [BIBM 2015 Workshop on Biomedical Visual Search and Deep Learning]

    Vincent, N., Stier, N., Yu, S., Liebeskind, D.S., Wang, D.J. and Scalzo, F. 2015.

    Detection of hyperperfusion on arterial spin labeling using deep learning. In Bioinformatics and Biomedicine (BIBM) 2015.

    Link to IEEE Explore