ABOUT ML: Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles

TL;DR

The paper argues that ML transparency is essential but lacks practical, standardized documentation practices. It proposes a documentation-driven approach and introduces ABOUT ML as an iterative, multi-stakeholder effort to codify best practices into templates. It surveys demand for transparency, reviews existing templates, and outlines governance to incorporate diverse perspectives and public feedback. The work aims to accelerate the adoption of actionable documentation standards to improve accountability, trust, and responsible ML deployment.

Abstract

We present the "Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles" (ABOUT ML) project as an initiative to operationalize ML transparency and work towards a standard ML documentation practice. We make the case for the project's relevance and effectiveness in consolidating disparate efforts across a variety of stakeholders, as well as bringing in the perspectives of currently missing voices that will be valuable in shaping future conversations. We describe the details of the initiative and the gaps we hope this project will help address.

Figures (1)

• Figure 1: Overview of ABOUT ML project's lifecycle.