AI for the Social Good (Dagstuhl Seminar 19082)

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

Thumbnail PDF


  • Filesize: 8.19 MB
  • 12 pages

Document Identifiers

Author Details

Claudia Clopath
Ruben De Winne
Mohammad Emtiyaz Khan
Tom Schaul
and all authors of the abstracts in this report

Cite AsGet BibTex

Claudia Clopath, Ruben De Winne, Mohammad Emtiyaz Khan, and Tom Schaul. AI for the Social Good (Dagstuhl Seminar 19082). In Dagstuhl Reports, Volume 9, Issue 2, pp. 111-122, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


Artificial intelligence (AI) and machine learning (ML) have made impressive progress in the last few 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, 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 workshop, 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. We identified ten key challenges for AI for Social Good initiatives. To make matters concrete, 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.
  • Machine Learning
  • Artificial Intelligence
  • Social Good
  • NGO
  • sustainable development goals
  • Non-governmental organisation


  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    PDF Downloads
Questions / Remarks / Feedback

Feedback for Dagstuhl Publishing

Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail