ΑΙhub.org
 

The Partnership on AI launches initiative to enhance machine learning transparency


by
01 May 2019



share this:

The Partnership on AI has announced an initiative to define best practices for transparency in machine learning.

The initiative aims to produce best practices around the considerations, reflections, and documentation necessary to prompt a thoughtful process of creating and understanding machine learning systems that account for how the technology impacts all parties—including the public at large, differentially affected communities, policymakers, and users.

The effort is called ABOUT ML for “Annotation and Benchmarking on Understanding and Transparency of Machine learning Lifecycles”. ABOUT ML will kick off with the publication of “draft v0” recommendations on ML lifecycle transparency this July, followed by successive drafts integrating lessons learned and feedback from the community.

You can read more about ABOUT ML on the Partnership on AI blog.




AIhub is dedicated to free high-quality information about AI.
AIhub is dedicated to free high-quality information about AI.

            AIhub is supported by:



Subscribe to AIhub newsletter on substack



Related posts :

What I’ve learned from 25 years of automated science, and what the future holds: an interview with Ross King

  30 Mar 2026
We launch our new series with a conversation with Ross King - a pioneer in the field of AI-enabled scientific discovery.

A multi-armed robot for assisting with agricultural tasks

and   27 Mar 2026
How can a robot safely manipulate branches to reveal hidden flowers while remaining aware of interaction forces and minimizing damage?

Resource-constrained image generation and visual understanding: an interview with Aniket Roy

  26 Mar 2026
Aniket tells us about his research exploring how modern generative models can be adapted to operate efficiently while maintaining strong performance.

RWDS Big Questions: how do we highlight the role of statistics in AI?

  25 Mar 2026
Next in our series, the panel explores the statistical underpinning of AI.

A history of RoboCup with Manuela Veloso

  24 Mar 2026
Find out how RoboCup got started and how the competition has evolved, from one of the co-founders.

Information-driven design of imaging systems

  23 Mar 2026
Framework that enables direct evaluation and optimization of imaging systems based on their information content.

Machine learning framework to predict global imperilment status of freshwater fish

  20 Mar 2026
“With our model, decision makers can deploy resources in advance before a species becomes imperiled.”

Interview with AAAI Fellow Yan Liu: machine learning for time series

  19 Mar 2026
Hear from 2026 AAAI Fellow Yan Liu about her research into time series, the associated applications, and the promise of physics-informed models.



AIhub is supported by:







Subscribe to AIhub newsletter on substack




 















©2026.02 - Association for the Understanding of Artificial Intelligence