“AI for Population Health”

As exemplified by the COVID-19 pandemic, our health and wellbeing depend on a difficult-to-measure web of societal factors and individual behaviors. AI can help us untangle this web and optimize interventions to improve health at a population level, especially for marginalized groups. However, population health applications raise new computational challenges, requiring us to make sense of limited data and optimize decisions under the resulting uncertainty. This talk presents methodological developments in machine learning, optimization, and social networks which are motivated by on-the-ground collaborations on HIV prevention, tuberculosis treatment, and the COVID-19 response. These projects have produced deployed applications and policy impact. For example, I will present the development of an AI-augmented intervention for HIV prevention among homeless youth. This system was deployed and evaluated in a field test enrolling over 700 youth and found to significantly reduce the prevalence of key risk behaviors for HIV.

"Exposure to News in the Digital Age"

Join Sandra González-Bailón, associate professor in the Annenberg School for Communication at the University of Pennsylvania, for her talk, where she will discuss the implications of the divide between informed citizens and news avoiders, and the need to measure exposure across media channels to identify populations that are most likely to be vulnerable to misinformation campaigns.

“Doing Good with Data: Fairly and Equitably”

Can AI, ML and Data Science help help prevent children from getting lead poisoning? Can it help reduce police violence and misconduct? Can it improve vaccination rates? Can it help cities better prioritize limited resources to improve lives of citizens and achieve equity? We’re all aware of the potential of ML and AI but turning this potential into tangible social impact, and more importantly equitable social impact, takes cross-disciplinary training, new methods, and collaborations with governments and non profits. I’ll discuss lessons learned from working on 50+ projects over the past few years with non-profits and governments on high-impact public policy and social challenges in criminal justice, public health, education, economic development, public safety, workforce training, and urban infrastructure. I’ll highlight opportunities as well as challenges around explainability and bias/fairness that need to tackled in order to have social and policy impact in a fair and equitable manner.

“AI for Public Health and Conservation: Learning and Planning in the Data-to-Deployment Pipeline”

With the maturing of AI and multiagent systems research, we have a tremendous opportunity to direct these advances towards addressing complex societal problems. We focus on the problems of public health and wildlife conservation, and present research advances in multiagent systems to address one key cross-cutting challenge: how to effectively deploy our limited intervention resources in these problem domains. We present our deployments from around the world as well as lessons learned that we hope are of use to researchers who are interested in AI for Social Impact. Achieving social impact in these domains often requires methodological advances; we will highlight key research advances in topics such as computational game theory, multi-armed bandits and influence maximization in social networks for addressing challenges in public health and conservation. In pushing this research agenda, we believe AI can indeed play an important role in fighting social injustice and improving society.

CSRAI will be accepting proposals for its inaugural seed funding program through Oct. 15 with projects expected to start in January 2021 and last for up to two years.