Our Blog

ABOUT ML Origin Story

Annotation and Benchmarking on Understanding and Transparency of Machine learning Lifecycles (ABOUT ML) is a project of the Partnership on AI (PAI) working towards establishing new norms on transparency via identifying best practices for documenting and characterizing key components and phases throughout the ML system lifecycle from design to deployment, including annotations of data, algorithms, performance, and maintenance requirements.

Hanna Wallach, Meg Mitchell, Jenn Wortman Vaughan and Timnit Gebru had a series of meetings given their work in documentation and standardization. These efforts include seminal research related to Datasheets for Datasets and Model Cards for Model Reporting. After those initial discussions coinciding with the early days of PAI (circa 2018 -2019), Hanna and Meg approached PAI and suggested that this work be continued and advanced under the umbrella of the multistakeholder organization and with the continued support and input of the Partner community.

Francesa Rossi and Kush Varshney, both from IBM, also approached PAI as a Partner, with the idea to focus on documentation work, and contributed to the early and ongoing efforts of ABOUT ML. IBM’s research related to Factsheets was meaningful to this practical effort. PAI has since continued to work with tech companies, nonprofits, academic researchers, policymakers, end users, and impacted nonusers to coordinate and influence practice in the ML documentation space. Eric Horvitz at Microsoft was also a key contributor in identifying the need to unify all of these projects bringing datasheets and model cards and other documentation practices and templates together to inspire the research focus for a single PAI program.

Jingying Yang was PAI’s original Program Lead for the ABOUT ML work. She, along with other staff members within PAI developed a research plan for how to engage with the stakeholders in order to set a new industry norm of documenting all ML systems built and deployed, thus changing practice at scale. Important contributors during this stage of the work included PAI Fellows Deb Raji and Alice Xiang, Head of Fairness, Transparency, and Accountability Research who served as PAI editors of the v0 foundational document. Hanna Wallach, Meg Mitchell, Jenn Wortman Vaughan and Timnit Gebru continued their pivotal support along with Lassana Magassa in shaping the program’s intentions and heightening awareness of important concepts related to attribution and inclusion.

Through an evidence/research-based multi-pronged initiative that includes and responds to solicited feedback from many stakeholders, the ABOUT ML work has progressed and the ultimate goal is to bring companies and organizations together with similar ideas around AI Documentation in an effort to push for general guidelines and an overall higher bar of Responsible AI. The impact we believe this work has and will continue to have is helping to create an organizational infrastructure for ethics in ML and helping to increase responsible tech development and deployment via transparency and accountability.

The work continues and we welcome the input of the AI community in the ongoing revisions to our foundational document as well as the artifacts and templates we plan to share as a result of that work. We have listed several other contributors to this effort on an internal website and ask that you visit this list and help us to add to it with names of other supporters, reviewers, researchers and contributors in the ABOUT ML effort by filling out this form.