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 :

Interactive World Simulator for Robot Policy Training and Evaluation

  17 Jul 2026
Yixuan Wang discusses his faithful world simulator that allows robots to learn how to push, pick up, and grasp objects.

#ICML2026 social media round-up

  17 Jul 2026
We take a look at what the participants got up to in Seoul.

François Pachet on music generation with AI

  16 Jul 2026
“The day I hear a song of the quality of the Beatles, I will say: ‘Okay, we are done’. And I’ve never heard anything like that. Never.”

AI for science – talk recordings now available to watch

  15 Jul 2026
Watch the invited talks from the day on YouTube.

AAAI presidential panel – factuality and trustworthiness

  14 Jul 2026
Watch the latest panel discussion in the series based on the Future of AI research report from AAAI.

The secret to human ‘brilliance’ that AI just can’t match

  13 Jul 2026
New research reveals how people learn social conventions with minimal data – and why that sets us apart from LLMs.

Pre-training isn’t bitter enough

  10 Jul 2026
Given an unlabeled data stream, and a small set of verifiable downstream examples, can we use those examples during continued pre-training?

Interview with Thi Kieu Khanh Ho: Time-series anomaly detection

  09 Jul 2026
How can we teach AI systems to recognize when something unusual or abnormal is happening in complex, real-world data streams, without relying on large amounts of labeled examples?



AUAI is supported by:







Subscribe to AIhub newsletter on substack




 















©2026.05 - Association for the Understanding of Artificial Intelligence