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 career was sparked by the amazing folks at 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


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


  • I worked as a Research Scientist intern within the algorithmic bias area at Spotify Research this summer.
  • I served as a subreviewer for NeurIPS 2019.
  • I attended ACM FAT* 2019 in Atlanta, GA.
  • I served as a member of the PC at the Black in AI workshop at NeurIPS 2018 & 2019.