NextBillion - An Initiative of the William Davidson Institute at the University of Michigan

Our workshop “AI for Social Good” will focus on applying artificial intelligence to solve problems important for society. The focus is on machine learning for the following areas: education, protecting democracy, urban planning, assistive technology for people with disabilities, health, agriculture, environmental sustainability, social welfare and justice and, sustainable development We believe that these fields are those where AI can have its strongest impact on society by reducing human suffering and improving democratic institutions. This workshop builds on our AI for Social Good workshop at NeurIPS 2018.

If managed correctly, the rapidly expanding field of AI has the potential to improve many aspects of our lives. However, two main problems arise when attempting to tackle social issues. First, there is often little incentive for researchers to tackle social problems as there are few conferences and journals that explicitly deal with such issues. Second, it is also difficult for researchers seeking to have a social impact to find problems to address. The convening of this workshop addresses these problems by networking impactful researchers and providing a venue for presentation.

This workshop brings together machine learning researchers, social impact leaders, stakeholders, government and policy leaders, and philanthropists to present and discuss ideas and applications linked to social issues, similarly to the AI Commonsproject. We are partnering with AI Commons so that accepted proposals are invited to submit their work there. Moreover, the workshop inspires the creation of new tools by the community to tackle critical problems. We also wish to promote the sharing of information and datasets that might prove relevant to researchers who share our goals.

We invite contributions relating to at least one of the previously mentioned domains. The models or approaches presented do not necessarily need to be of outstanding theoretical novelty, but should demonstrate potential for a strong social impact. We especially encourage work where machine learning and in particular representation learning could meaningfully amplify existing efforts for social good.