ABOUT ML

  • Identifying Topics
  • Convening Stakeholders
  • Collecting Insights
  • Developing Resources
  • Engaging Audiences

Stay Informed





please write the name of your organization in full


Hidden Fields

Subscription completed successfully.
Validation error occurred, please confirm the fields and submit again.
Oops, Sorry. Something is wrong. Please try again later.

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.

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

Designing ABOUT ML pilots
Running + Testing ABOUT ML pilots

Implement and scale what works

 

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 – To Be Announced

The formal, active, and engaged body of Steering Committee members has been significantly truncated and the members of this more intimate and consistently engaged group have been consulted and notified. We look forward to announcing their participation in the near future

Origin Story

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.

Webinar: Understanding the Value of ML Documentation