AI for social good: unlocking the opportunity for positive impact

By Nenad Tomašev, Julien Cornebise, Frank Hutter, S. Mohamed, A. Picciariello, Bec Connelly, D. Belgrave, Daphne Ezer, Fanny Cachat van der Haert, F. Mugisha and 14 more. 2020

296 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Modeling and Simulation, Safety Research, Safety, Robustness & Interpretability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Carole-Jean Wu
2. Bilge Acun
3. Myle Ott
4. Michael G. Rabbat
5. William S. Isaac
6. Shakir Mohamed
7. Parthasarathy Ranganathan
8. Kim M. Hazelwood

In Safety Research

1. William S. Isaac
2. Shakir Mohamed
3. Marie-Therese Png

In Safety, Robustness & Interpretability

1. William S. Isaac
2. Henrik Skaug Sætra
3. Shakir Mohamed
4. Marie-Therese Png
5. Carole-Jean Wu
6. Myle Ott

Citations in the corpus

• Sustainable AI: Environmental Implications, Challenges and Opportunities (2021, background): “Despite the positive societal benefits [20], the endless pursuit of achieving higher model quality has led to the exponential scaling of AI with significant energy and environmental footprint implications.”
• Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence (2020, background): “…is in economic and social development where advanced technologies are propounded as solutions for complex developmental scenarios, represented by the growing areas of AI for Good and AI for the Sustainable Development Goals (AI4SDGs) (Vinuesa et al. 2020; Floridi et al. 2018; Tomašev et al. 2020).”
• A Framework for Evaluating and Disclosing the ESG Related Impacts of AI with the SDGs (2021, methodology): “More recently, some have used the term AI for social good (AI4SG) to describe work on AI aimed at the SDGs [35].”
• Artificial Intelligence (AI) Student Assistants in the Classroom: Designing Chatbots to Support Student Success (2022, background): “How then, as instructors, can we provide needed interactions without working overtime? Artificial intelligence (AI) has provided promising opportunities for addressing important problems in education and society (Cowls et al., 2021; Tomašev et al., 2020) while also provoking considerations about the”
• Socio-Technological Challenges and Opportunities: Paths Forward (2021, background): “…the IT ecosystem, this cost must be evaluated holistically [Chang et al., 2010, Bardon et al., 2020, Gupta et al., 2021, Patterson et al., 2021] in light of the benefits such an ecosystem can provide in other domains [Hager et al., 2019, Tomasev et al., 2020, Mulhern, 2021, United Nation, 2021].”