17. – 22. Februar 2019, Dagstuhl-Seminar 19082

AI for the Social Good


Claudia Clopath (Imperial College London, GB)
Ruben De Winne (Oxfam Novib – The Hague, NL)
Mohammad Emtiyaz Khan (RIKEN – Tokyo, JP)
Tom Schaul (Google DeepMind – London, GB)

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Dagstuhl Report, Volume 9, Issue 2 Dagstuhl Report
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Programm des Dagstuhl-Seminars [pdf]

Press Room

The Dagstuhl declaration on the application of machine learning and artificial intelligence for Social Good


The purpose of Dagstuhl Seminar 19082: AI for the Social Good was to bring together researchers in artificial intelligence (AI) and machine learning (ML) with non-governmental organisations (NGOs) to explore if and how AI and ML could benefit the social good. Indeed, AI and 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. But as part of a wider AI for Social Good trend, this seminar wanted to contribute to ensuring that the social good does not become an afterthought in the rapid AI and ML evolution, but that society benefits as a whole.

The five-day seminar brought together AI and ML researchers from various universities and industry research labs with representatives from NGOs based in Somalia, Rwanda, Uganda, Belgium, United Kingdom and The Netherlands. These NGOs all pursue various social good goals, such as increasing access to justice for vulnerable people, promoting human rights & protecting human rights defenders, and defeating poverty. On these topics, NGOs have rich domain knowledge, just like they have vast networks with (non-)governmental actors in developing countries. Mostly, NGOs have their finger on the pulse of the challenges that the world & especially its most vulnerable inhabitants are facing today, and will be facing tomorrow. The objective of the seminar was to look at these challenges through an AI and ML lens, to explore if and how these technologies could help NGOs to address these challenges. The motivation was also that collaborations between AI and ML researchers and NGOs could 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.

The seminar facilitated the exploration of possible collaborations between AI and ML researchers and NGOs through a two-pronged approach. This approach combined high-level talks & discussions on the one hand with a hands-on hackathon on the other hand. High-level talks & discussions focused first on the central concepts and theories in AI and ML and in the NGOs’ development work, before diving into specific issues such as privacy & anonymity, data quality, intellectual property, accessibility and ethical issues. These talks and discussions allowed all participants - in a very short timeframe - to reach a sufficient level of understanding of each other's work. This understanding was the basis to then start investigating jointly through a hackathon how AI and ML could help addressing the real-world challenges presented by the NGOs. At the start of the hackathon, an open marketplace-like setting allowed AI and ML researchers and NGOs to find the best match between technological supply and demand. When teams of researchers and NGOs were established, their initial objective was not to start coding, but to define objectives, assess scope and feasibility. Throughout the hackathon, group membership was fluid, as some projects finished early, were deemed out of scope, or needed to wait for data. Some groups managed to build a viable initial prototype, others established the seeds for future collaborations, and a few were proposed as full summer projects within the ``Data Science of Social Good summer school''. The projects' aims were diverse. They included better seeds for farmers, modelling cognitive age and decline, scalable legal assistance and scalable citizen feedback. As a result of the hackathon, all NGOs could take concrete results home - some to build on further, some as mature solutions.

Finally, a result of the seminar that is relevant for the entire AI for Social Good community are the ten key challenges for AI for Social Good initiatives that participants identified:

  1. the importance of deep, long-term partnerships,
  2. clear and well-defined goals and use cases,
  3. bias towards simpler solutions,
  4. data readiness,
  5. setting expectations with regards to both impact and the pace at which technology can be applied,
  6. ensuring privacy and security of data,
  7. inclusivity and ethics of the applications,
  8. factoring in the limitations of both communities,
  9. challenges in overcoming the barriers to NGOs utilising the potential of AI/ML, and
  10. the relative cost of AI/ML for social good.
Summary text license
  Creative Commons BY 3.0 Unported license
  Ruben De Winne

Related Dagstuhl-Seminar


  • Artificial Intelligence / Robotics
  • Society / Human-computer Interaction


  • Artificial intelligence
  • Machine learning
  • Non-governmental organizations
  • International development
  • Humanitarian


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