2 Search Results for "De Winne, Ruben"

Document
DOI: 10.4230/DagRep.12.2.134
AI for the Social Good (Dagstuhl Seminar 22091)

Authors: Claudia Clopath, Ruben De Winne, and Tom Schaul

Published in: Dagstuhl Reports, Volume 12, Issue 2 (2022)

Abstract
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.
Cite as

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)

Copy BibTex To Clipboard

@Article{clopath_et_al:DagRep.12.2.134,
  author =	{Clopath, Claudia and De Winne, Ruben and Schaul, Tom},
  title =	{{AI for the Social Good (Dagstuhl Seminar 22091)}},
  pages =	{134--142},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2022},
  volume =	{12},
  number =	{2},
  editor =	{Clopath, Claudia and De Winne, Ruben and Schaul, Tom},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.12.2.134},
  URN =		{urn:nbn:de:0030-drops-169345},
  doi =		{10.4230/DagRep.12.2.134},
  annote =	{Keywords: Machine Learning, Artificial Intelligence, Social Good, NGO, sustainable development goals, Non-governmental organisation}
}
Document
DOI: 10.4230/DagRep.9.2.111
AI for the Social Good (Dagstuhl Seminar 19082)

Authors: Claudia Clopath, Ruben De Winne, Mohammad Emtiyaz Khan, and Tom Schaul

Published in: Dagstuhl Reports, Volume 9, Issue 2 (2019)

Abstract
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.
Cite as

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)

Copy BibTex To Clipboard

@Article{clopath_et_al:DagRep.9.2.111,
  author =	{Clopath, Claudia and De Winne, Ruben and Khan, Mohammad Emtiyaz and Schaul, Tom},
  title =	{{AI for the Social Good (Dagstuhl Seminar 19082)}},
  pages =	{111--122},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2019},
  volume =	{9},
  number =	{2},
  editor =	{Clopath, Claudia and De Winne, Ruben and Khan, Mohammad Emtiyaz and Schaul, Tom},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.9.2.111},
  URN =		{urn:nbn:de:0030-drops-108620},
  doi =		{10.4230/DagRep.9.2.111},
  annote =	{Keywords: Machine Learning, Artificial Intelligence, Social Good, NGO, sustainable development goals, Non-governmental organisation}
}
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