Hosted by the Transdisciplinary Institute in Applied Data Sciences (TRIADS)

How do we make choices when faced with a health crisis? Or a natural disaster? Data-centric technology and AI are being developed and deployed to help us navigate a new route, summarize large collections of documents, or protect our devices using face recognition, but the gap between autonomous decision-making and human decision-making still exists. We are surrounded by data, but most people are not yet comfortable having data science and/or AI as part of their work or daily lives (even if they’re already using it). Moreover, people tend to make decisions under uncertain, dynamic, and resource-constrained circumstances and often (sometimes unknowingly) incorporate concerns about risk, equity and trust. Tricky to capture in a prediction model. This disconnect is contributing to growing disparities in use across demographic groups and sectors. Data science and AI education have never been more important.

In this talk, we’ll look at the data science and AI education efforts of two large NSF-funded initiatives centrally housed at Carnegie Mellon University – the NSF AI Institute for Societal Decision Making and the sports-analytics focused SCORE network to build pipelines and train the public – including K-12, community college, undergraduates, graduate students, and the workforce – to use data science and AI to positively impact society.

For inquiries contact TRIADS@wustl.edu.