Transparency

Transparency, at its heart, is a clear, easily understandable, and plain language explanation of what something is, what it does, and why it does that.

For dataset documentation, transparency means providing a window into the dataset's quality, validity, reproducibility, and riskā€”all in a way that's accessible to the various audiences who may interact with it, including developers, business stakeholders, downstream users, and more.

Data Cards

Data Cards are people-centric summaries of transparent dataset documentation.

They offer a structured way to document datasets and encourage informed decisions about the data used in AI systems for product and research. But there is no one-size-fits-all template. That's where the Data Cards Playbook comes in.

See Data Cards examples

Credits

Core Team

Emily Brouillet, Reena Jana, Oddur Kjartansson, Dan Nanas, Mahima Pushkarna (co-lead), Danielle Smalls, Vivian Tsai, Andrew Zaldivar (co-lead)

Special Thanks

Lucas Ackerknecht, Hartwig Adam, Seiji Armstrong, Lora Aroyo, Sebastian Assaf, Anurag Batra, Samy Bengio, Thomas Cadwalader, Michelle Carney, Will Carter, Amanda Casari, Di Dang, Alex David Norton, Tiffany Deng, Emily Denton, Tulsee Doshi, Patrick Gage Kelley, Timnit Gebru, Robbie Gonzalez, Alex Hanna, Jing Hua, Ben Hutchinson, Nathan Ie, Robyn Im, Orion Jankowski, Shivani Kapania, David Karam, Daniel Kim, Leslie Lai, Eryka Lehr, Elijah Logan, Daphne Luong, Nicole Maffeo, Meg Mitchell, Maysam Moussalem, Unni Nair, Ricardo Olenewa, Kristen Olson, Praveen Paritosh, Angie Peng, Ludovic Peran, Ravi Rajakumar, Susanna Ricco, Kevin Robinson, Taylor Roper, Mo Shomrat, Andrew Smart, Jamila Smith-Loud, Joseph Thomas, Bobby Tran, Aybuke Turker, Fernanda Viegas, James Wang, Martin Wattenberg, James Wexler, Catherine Williams, Catherina Xu, Tabitha Yong, Ben Zevenbergen