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ABOUT ML - Partnership on AI
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Partnership on AI is a multi-stakeholder organization; this page describes one of their working groups focused on responsible ML practices, relevant for those tracking AI governance initiatives and industry self-regulation efforts.
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Summary
This page describes the Partnership on AI's Machine Learning workstream, which focuses on advancing responsible AI research and practices across member organizations. The workstream brings together AI researchers and practitioners to address key challenges in making ML systems safer, more reliable, and beneficial.
Key Points
- •PAI's ML workstream facilitates collaboration between industry, academia, and civil society on responsible AI development
- •Focus areas include fairness, transparency, and accountability in machine learning systems
- •Workstream produces research, best practices, and guidelines to shape responsible AI norms
- •Brings together diverse stakeholders to identify and address emerging AI safety and ethics challenges
1 FactBase fact citing this source
| Entity | Property | Value | As Of |
|---|---|---|---|
| Partnership on AI | program | ABOUT ML — framework and resources for transparency reporting on machine learning systems | 2023 |
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ABOUT ML - Partnership on AI
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ABOUT ML
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Use the field below to share any thoughts or questions you have on the initiative. If you would like to advise on this work or suggest someone we should reach out to, please say a few words about yourself or the person you’re suggesting.
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For any questions, email communications@partnershiponai.org.
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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 .
ABOUT ML Resources Library
A library of resources designed to help organizations and individuals begin implementing AI/ML transparency at scale.
Explore the Resources
Updates
Blog
Fairness, Transparency, and Accountability/ABOUT ML
How Better AI Documentation Practices Foster Transparency in Organizations
Albert Tanjaya
Jun 06, 2024
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Blog
Fairness, Transparency, and Accountability/ABOUT ML
Improving Documentation in Practice: Our First ABOUT ML Pilot
Jiyoo Chang
Oct 11, 2022
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Blog
Fairness, Transparency, and Accountability/ABOUT ML
Making It Easier to Compare the Tools for Exp
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