ABOUT ML: Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles
Deborah I. Raji 111Equal contribution. Partnership on AI San Francisco, CA Jingying Yang ††Partnership on AI San Francisco, CA
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.
中文速览
让AI系统变得更透明、让人们真正理解机器学习模型在做什么，是当前技术治理领域最迫切的挑战之一，而"只喊口号、缺乏落地"的困境一直阻碍着进展。ABOUT ML（机器学习生命周期理解与透明度的标注与基准）项目的核心思路是：用标准化文档（ML documentation）来将"透明度"这一抽象原则转化为可操作的工程实践，就像食品营养标签让消费者看懂食品成分一样，让开发者系统记录模型的数据来源、预期用途、风险与局限。项目借鉴互联网标准制定（W3C等）的多方协作模式，组建了涵盖学界、企业、非营利机构和公民社会的30人指导委员会，并引入"多元声音"（Diverse Voices）方法论，专门纳入历史上被技术决策边缘化群体的反馈。该项目的重要意义在于，它首次尝试将来自不同组织的零散文档实践整合为统一的行业指南，有望填补现有研究在标准化、包容性和可实施性上的空白，推动AI透明度从理念走向真正可核查的现实。
原文 arXiv:1912.06166;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1912.06166v3