AI for social good: unlocking the opportunity for positive impact

• Published in: Nature communications Vol. 11; no. 1; pp. 2468 - 6
• Main Authors: Tomašev, Nenad, Cornebise, Julien, Hutter, Frank, Mohamed, Shakir, Picciariello, Angela, Connelly, Bec, Belgrave, Danielle C. M., Ezer, Daphne, Haert, Fanny Cachat van der, Mugisha, Frank, Abila, Gerald, Arai, Hiromi, Almiraat, Hisham, Proskurnia, Julia, Snyder, Kyle, Otake-Matsuura, Mihoko, Othman, Mustafa, Glasmachers, Tobias, Wever, Wilfried de, Teh, Yee Whye, Khan, Mohammad Emtiyaz, Winne, Ruben De, Schaul, Tom, Clopath, Claudia
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 18.05.2020; Nature Publishing Group; Nature Portfolio
• Subjects: 119/118; 706/134; 706/648/179; Artificial intelligence; Domains; Guidelines; Humanities and Social Sciences; Learning algorithms; Machine learning; multidisciplinary; Partnerships; Perspective; Researchers; Science; Science (multidisciplinary); Social impact; Subject specialists; Sustainable development
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-020-15871-z

Advances in machine learning (ML) and artificial intelligence (AI) present an opportunity to build better tools and solutions to help address some of the world’s most pressing challenges, and deliver positive social impact in accordance with the priorities outlined in the United Nations’ 17 Sustainable Development Goals (SDGs). The AI for Social Good (AI4SG) movement aims to establish interdisciplinary partnerships centred around AI applications towards SDGs. We provide a set of guidelines for establishing successful long-term collaborations between AI researchers and application-domain experts, relate them to existing AI4SG projects and identify key opportunities for future AI applications targeted towards social good. The AI for Social Good movement aims to apply AI/ML tools to help in delivering on the United Nations’ sustainable development goals (SDGs). Here, the authors identify the challenges and propose guidelines for designing and implementing successful partnerships between AI researchers and application - domain experts.