AI and ML have made impressive progress in the last few years. Long-standing challenges like the game of GO have fallen and the technology has entered daily use via the vision, speech or translation capabilities in billions of smartphones, and more recently via general uptake of software applications built on large language models. 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 Dagstuhl 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 will bring together AI and ML researchers from various universities with representatives from NGOs pursuing various social good goals, such as providing legal aid, providing humanitarian assistance, advocating for gender justice, denouncing growing levels of inequality, 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 is 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 is 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 will facilitate the exploration of possible collaborations between AI and ML researchers and NGOs through a two-pronged approach. This approach will combine high-level talks & discussions on the one hand with a hands-on hackathon on the other hand. High-level talks & discussions will focus first on the central concepts and theories in AI and ML and in the NGOs’ development work, before diving into specific issues such as generalizability, data pipelines, and explainability. These talks and discussions will allow all participants – in a very short timeframe – to reach a sufficient level of understanding of each other’s work. This understanding will be 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 will allow AI and ML researchers and NGOs to find the best match between technological supply and demand. When teams of researchers and NGOs will be established, their initial objective will not be to start coding, but to define objectives, assess scope and feasibility.

The intense exchanges during the hackathon will allow NGOs with a lower AI/ML maturity to increase understanding of the capabilities of AI/ML and define actions to effectively start working with AI/ML. NGOs that already have a more advanced understanding and use of AI/ML technology prior to the seminar, could take their AI maturity to the next level by trying out new ML approaches, designing and testing tailored ML models, or simply exploring new partnerships. Key to this success of the hackathon – and the seminar at large – is the presence of AI/ML experts whose respective fields of expertise that will be matched with the various needs of the various NGOs. This group composition also will facilitate a productive discussion about topics that cut across the AI for social good field, such as how to properly evaluate AI models that are used for good.

Creative Commons BY 4.0

Claudia Clopath, Marc Deisenroth, Parvathy Krishnan, and Jacopo Margutti