AI for the Social Good (Dagstuhl Seminar 22091)

Authors Claudia Clopath, Ruben De Winne, Tom Schaul and all authors of the abstracts in this report

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Author Details

Claudia Clopath
  • Imperial College London, GB
Ruben De Winne
  • Oxfam Novib - The Hague, NL
Tom Schaul
  • Google DeepMind - London, GB
and all authors of the abstracts in this report

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Claudia Clopath, Ruben De Winne, and Tom Schaul. AI for the Social Good (Dagstuhl Seminar 22091). In Dagstuhl Reports, Volume 12, Issue 2, pp. 134-142, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)


Progress in the field of Artificial intelligence (AI) and machine learning (ML) has not slowed down in recent years. Long-standing challenges like Go have fallen and the technology has entered daily use via the vision, speech or translation capabilities in billions of smartphones. The pace of research progress shows no signs of slowing down, and demand for talent is unprecedented. AI for Social Good in general is trying to ensure that the social good does not become an afterthought, but that society benefits as a whole. In this Dagstuhl Seminar, which can be considered a follow-up edition of Dagstuhl Seminar 19082, we brought together AI and machine learning researchers with non-governmental organisations (NGOs), as they already pursue a social good goal, have rich domain knowledge, and vast networks with (non-)governmental actors in developing countries. Such collaborations benefit both sides: on the one hand, the new techniques can help with prediction, data analysis, modelling, or decision making. On the other hand, the NGOs' domains contain many non-standard conditions, like missing data, side-effects, or multiple competing objectives, all of which are fascinating research challenges in themselves. And of course, publication impact is substantially enhanced when a method has real-world impact. In this seminar, researchers and practitioners from diverse areas of machine learning joined stakeholders from a range of NGOs to spend a week together. We first pursued an improved understanding of each side’s challenges and established a common language, via presentations and discussion groups. Building on this foundation, we organised a hackathon around some existing technical questions within the NGOs to scope the applicability of AI methods and seed collaborations. Finally, we defined guidelines and next steps for future AI for Social Good initiatives.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Machine learning
  • Machine Learning
  • Artificial Intelligence
  • Social Good
  • NGO
  • sustainable development goals
  • Non-governmental organisation


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