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

TL;DR

The ABOUT ML project aims to establish standardized documentation and benchmarking practices to improve transparency and understanding of machine learning lifecycles, involving diverse stakeholders.

Contribution

It introduces a comprehensive initiative to operationalize ML transparency through annotation and benchmarking, fostering standardization and inclusivity.

Findings

Consolidates efforts across stakeholders

Highlights importance of diverse perspectives

Proposes a framework for ML lifecycle documentation

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.