ΑΙhub.org
 

Advancing data justice research and practice project


by
31 March 2023



share this:

black, white and grey hands with text in the backgroundScreenshot from the data justice video.

Advancing data justice research and practice is a collaboration between the Global Partnership on AI (GPAI), The Alan Turing Institute, 12 policy pilot partners, and participants and communities across the globe. The project aims to augment the current thinking around data justice and to provide actionable resources that will help policymakers, practitioners, and impacted communities.

As part of the project, a short series of documentaries tracks the work of the participants. The first instalment was published last year and discusses how data-driven technologies can be deployed in a way which is compatible with values of social justice.

The second episode of this series has recently been released, and you can watch it below. It defines data injustice and explores some case studies of the human consequences of such injustice.

A major contributions of the project has been the production of a series of three practical guides for policymakers, impacted communities, and developers. The guides consist of background content on data justice and how this relates to AI, as well as practical questions for stakeholder groups to consider in their practice, use, and experience of AI/ML systems.

You can find links to all of the guides here. The pdf versions are at these links:
Data Justice in Practice: A Guide for Policymakers
Data Justice in Practice: A Guide for Impacted Communities
Data Justice in Practice: A Guide for Developers

Find out more about the project here.




Lucy Smith is Senior Managing Editor for AIhub.
Lucy Smith is Senior Managing Editor for AIhub.

            AUAI is supported by:



Subscribe to AIhub newsletter on substack



Related posts :

Cybersecurity, evolutionary game theory, and AI safety: an interview with Adeela Bashir

  09 Oct 2026
In the latest of our interviews with the IJCAI-ECAI 2026 doctoral consortium participants, we learn about how cybersecurity attack and defence behaviours evolve.

2026 AAAI / ACM SIGAI Doctoral Consortium interviews compilation

  08 Oct 2026
We collate our interviews with the 2026 cohort of doctoral consortium participants.

Machine learning for clinical time-series forecasting: an interview with Mayra Elwes

  07 Oct 2026
Our series hearing from the IJCAI-ECAI doctoral consortium participants continues.

Can you teach yourself to detect AI writing? Maybe

  06 Oct 2026
Before generative AI, we could generally assume that written text had been composed by a human. This is no longer the case. So how can we spot AI-written text?

Interview with William Yijiang Li: vision language models and the physical world

  05 Oct 2026
How effective are vision language models at understanding how the physical world changes over time?

Forthcoming machine learning and AI seminars: October 2026 edition

  02 Oct 2026
A list of free-to-attend AI-related seminars that are scheduled to take place in the next couple of months.

Rebuilding the brain with neuromorphic computing: an interview with Oliver Rhodes

  01 Oct 2026
Neuromorphic computing takes inspiration from biology to build faster, more energy efficient systems.


↑


AUAI is supported by:







Subscribe to AIhub newsletter on substack




 















©2026.05 - Association for the Understanding of Artificial Intelligence