The Collab Challenges are a series of collaborative experiments launched by JournalismAI in 2021. They bring together media organisations from across the world to explore innovative solutions to improve journalism via the use of AI technologies. The programme was an evolution of the 2020 Collab, the first-ever collaboration of this kind.
Participants from more than 20 news organisations worldwide worked together to imagine and prototype new ideas to turbocharge journalism with AI, with the support of our regional partners: the at Northwestern University in the Americas, and in EMEA, and the at Bennett University in APAC.
In this page, you can explore all the projects of the 2021 Collab Challenges teams and learn more about this collaborative experience directly from our regional partners:
Participants: the Guardian (UK) and AFP (France)
Quotes are a key element of news articles. They help journalists explain events better and users form their own opinions. In this project, participants from the Guardian and AFP built a system that automatically extracts quotes from news articles and accurately attributes them to sources (people or organisations).
the team's presentation at the 2021 JournalismAI Festival
Participants: Il Sole 24 Ore (Italy), Deutsche Welle (Germany), Maharat Foundation (Lebanon), and Clwstwr (UK)
At the base of this cross-border project is the concept of modules (segments of journalistic discourse) that can be repurposed in different formats with the aid of an algorithm. The focus is on building modular-first news artefacts that are specifically created for the purpose of modularisation and show how modules can be configured and reconfigured to create stories on the same topic but meeting a range of user needs through different formats.
Explore the project at
the team's presentation at the 2021 JournalismAI Festival
Participants: Bloomberg (US), Data Crítica (Mexico), La Nación (Argentina), El CLIP (Latin America)
A picture can say a thousand words. That's why this project decided to explore the use of satellite imagery and applied AI for storytelling. The primary focus has been to look at the climate crisis through this lens to see what can be reported through the observation of our planet via satellite imagery.
the team's presentation at the 2021 JournalismAI Festival
Participants: BR AI + Automation Lab (Germany) and Science Media Center Germany
News articles often lack context, which is crucial for understanding the story. News formats are concise and avoid transporting redundant information, which excludes already underserved groups of potential readers. Participants of this team decided to tackle this problem by linking concepts, terms, and entities from news articles to a knowledge graph to offer relevant context and definitions for readers who might lack some important background information.
the team's presentation at the 2021 JournalismAI Festival
Participants: MuckRock (US), La Nación (Argentina), El CLIP (Latin America), Ojo Público (Peru)
The project aims to make it easy for journalists to quickly do rough classifications and sorting of large-scale document sets across a variety of languages. Building off prior document classification and reporting work at La Nación and a prototype machine learning tool developed by MuckRock, DockIns demonstrate new ways not only for newsrooms to quickly understand and map large corpus of documents, but also help automate ongoing accountability coverage.
the team's presentation at the 2021 JournalismAI Festival
Participants: AzMina (Brazil), Data Crítica (Mexico), El Clip (Latin America), La Nación (Argentina)
This cross-border collaboration maps misogynistic attacks that are initiated or stimulated by political figures on Twitter. To do that, participants trained an AI model that is able to identify with a good level of assertiveness when a publication contains hate speech against women.
the team's presentation at the 2021 JournalismAI Festival
Participants: Media City Bergen