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




            AIhub is supported by:


Related posts :



UrbanTwin: seeing double for sustainability

A digital twin for urban infrastructure: assessing the effectiveness of climate-related policies and actions.
26 January 2023, by

Counterfactual explanations for land cover mapping: interview with Cassio Dantas

Cassio tells us about work applying counterfactual explanations to remote sensing time series data for land-cover mapping classification.
25 January 2023, by

Bottom-up top-down detection transformers for open vocabulary object detection

We introduce a model that detects all objects that a phrase mentions.
23 January 2023, by

The Good Robot Podcast: featuring Arjun Subramonian

In this episode, Eleanor and Kerry talk to Arjun Subramonian on queer approaches to AI and computing.
20 January 2023, by

Applying AI to pathology reveals insights in endometrial cancer diagnostics

Interpretable deep learning model to predict the molecular classification of endometrial cancer from slide images.
19 January 2023, by





©2021 - Association for the Understanding of Artificial Intelligence


 












©2021 - ROBOTS Association