Overview
ABOUT ML (Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles) is a multi-year, multi-stakeholder initiative led by PAI. This initiative aims to bring together a diverse range of perspectives to develop, test, and implement machine learning system documentation practices at scale.
The initiative is an ongoing, iterative process designed to co-evolve with the rapidly advancing field of AI development and deployment. In recognition that documentation is both an artifact and a process, ABOUT ML is structured into an artifact workstream and a process workstream.
Read the ABOUT ML Reference Document here.
ABOUT ML Resources Library
Updates
Research
Get Involved
Start Here
Contribute to future work
Our goal for 2021 is to design testable pilots in a multi stakeholder manner. To make this process more tractable, we’ve broken this into two different workstreams. Each button below leads to a key subtask in this process, and we invite you to share your thoughts, comments, and feedback on any that you are interested in.
Process workstream
Help us solve the challenge of how documentation can be created at scale within an organization.
Artifact workstream
Join the debate on what information stakeholders deserve to know about ML systems, and how that information should be presented.
Future Work
Deployed Examples of ML Documentation
See real-world deployed examples of ML documentation which can focus on datasets, models, and ML systems. Provide your feedback on these examples as part of ABOUT ML’s public feedback comment process.
To guide ABOUT ML, let the steering committee know what you think of these examples. Which questions are useful? What questions are these examples missing? Is there anything about the format of one of these examples that is effective? Leave a comment with the comment button on the left.
Advisors
The ABOUT ML group of advisors and experts is comprised of experts, researchers and practitioners recruited from a diverse set of PAI Partner organizations. We continue to provide meaningful updates and invitations for them to participate in the work. We are grateful for their contributions to this community work enabling responsible AI by increasing transparency and accountability with machine learning system documentation.
Himani Agrawal
Data Scientist
AT&T Research Labs
Norberto Andrade
Privacy & Public Policy Manager
Facebook
Amir Banifatemi
General Manager, Innovation & Growth
XPRIZE
Rachel Bellamy
Principal Researcher & Manager
Human-AI Collaboration IBM
Umang Bhatt
Student Fellow
Leverhulme Centre for The Future of Intelligence
Diane Chang
Distinguished Data Scientist
Intuit
Jacomo Corbo
Chief Scientist
Quantumblack
Daniel First
Associate/Data Scientist
Mckinsey/Quantumblack
Ben Garfinkel
Research Fellow
Future of Humanity Institute
Jeremy Gillula
Tech Projects Director
EFF
Jerremy Holland
Director of AI Research
Apple
Sara Jordan
Senior Counsel, AI & Ethics
Future of Privacy Forum
Joohyung Lee
Corporate VP/Head of Lab
Samsung
Brenda Leong
Senior Counsel & Director
of Strategy
Future of Privacy Forum
Tyler Liechty
Data Engineer
Deepmind
Lassana Magassa
Graduate Research Associate
Tech Policy Lab/University of Washington
Meg Mitchell
AI/ML Researcher
Amanda Navarro
Managing Director
PolicyLink
Sinead O’Brien
AI/ML Engagement Lead
BBC
Irina Raicu
Director, Internet Ethics Program
Markkula Center for Applied Ethics
Deborah Raji
Fellow
Mozilla Foundation
Becca Ricks
Researcher
Mozilla Foundation
Andrew Selbst
Postdoctoral Scholar
Data & Society
Ramya Sethuraman
Product Manager
Facebook
Moninder Singh
Research Staff Member
IBM
Spandana Singh
Policy Analyst
Open Technology Institute
Amber Sinha
Senior Programme Manager
Centre for Internet & Society
Michael Spranger
Senior Research Scientist
AI
Collaboration Office Sony
Andrew Strait
Associate Director of Research Partnerships
ADA Lovelace Institute
Hanna Wallach
Senior Principal Researcher
Microsoft
Adrian Weller
Senior Research Fellow
Leverhulme Center for the Future of Intelligence
Abigail Wen
Author & Filmmaker
Alexander Wong
Co-Director
Vision & Image Processing(VIP) Research Group(University of Waterloo)
Jennifer Wortman Vaughan
Senior Principal Researcher
Microsoft
Andrew Zaldivar
Senior Developer Advocate
Google
We also consult with many of our other Partners.
Steering Committee
Michael Hind
Distinguished Research Staff Member
IBM Research
Meg Mitchell
Research and Chief Ethics Scientist
Hugging Face
Kush R Varshney
Distinguished Research Scientist and Manager
IBM Research
Hanna Wallach
Partner Research Manager
Microsoft Research
Jennifer Wortman Vaughan
Senior Principal Researcher
Microsoft
Origin
As you explore ABOUT ML, we invite you to learn more about this work’s origins and the amazing researchers who helped from the very beginning.
Notably, Hanna Wallach, Meg Mitchell, Jenn Wortman Vaughan, Timnit Gebru, Lassana Magassa, and Jingying Yang were instrumental in the foundations of the work and we thank them for their significant contributions. Francesa Rossi and Kush Varshney, both from IBM, and Eric Horvitz at Microsoft were also key contributors in making this work possible. Please read more about the origins of ABOUT ML and contributors to the project below.
