Briana Vecchione

I'm currently an Information Science PhD student at Cornell University and supported by a Google Women Techmakers scholarship. My work uses causal, statistical, and other computational methods to identify and mitigate structural issues of algorithmic bias/discrimination, often related to public policy or similar human-centered domains. In the past, I've worked as a research scientist or fellow in spaces like Spotify and Microsoft. Much of my time is spent as an affiliate of AI Policy and Practice, Mechanism Design for Social Good, Queer in AI, Graduate Women in Science, and the Center for the Study of Inequality. I've also been a feature, keynote, or speaker in spaces like Bloomberg, Facebook, Sony, and the Metropolitan Museum of Art. In my spare time, I do my best to make time for travel, music, reading, and amateur photography.

Acknowledgements: My interests were sparked through the Microsoft Research Data Science Summer School and supported by opportunities through the National Science Foundation, the Association of Computing Machinery, and the Anita Borg Institute.

Talks & Workshops

  • ACM FAT* 2020 Doctoral Consortium
    Barcelona, Spain. January 29, 2020.
  • Digital Lives: Perspectives on Ethics and AI
    New York, NY. October 21, 2019.
  • Partnership on AI: ABOUT Machine Learning Workshop
    New York, NY. April 2, 2019.
  • Computing Research Association URMD Graduate Cohort
    Waikoloa, HI. March 22, 2019.
  • AI, Policy, and Practice Seminar
    Ithaca, NY. September 27, 2018.
    Datasheets for Datasets
  • Data for Good Exchange
    New York, NY. September 24, 2017.
    Building Open Data Dashboards for Hyper Local Government
  • AI Now: Experts Workshop
    Boston, MA. July 10, 2017.
  • ACM Richard Tapia Celebration of Diversity in Computing
    Boston, MA. February 15, 2015.
    Self Balancing CitiBikes
  • Knowledge Discovery and Data Mining at Bloomberg (KDD)
    New York, NY. August 24, 2014.
    Self Balancing CitiBikes
  • Microsoft Research Data Science Summer School
    New York, NY. August 2014.
    Self Balancing CitiBikes

Datasheets for Datasets (arxiv)
Short version appeared at FATML 2018
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford.

Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste
(under review)
Zahra Nazari, Christophe Charbuillet, Johan Pages, Martin Laurent, Denis Charrier, Briana Vecchione and Benjamin Carterette

Data Science Ethics (in progress)
Solon Barocas, Karen Levy, Sarah Riley, Briana Vecchione