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Human-Centered AI

The rapid growth in data generated on a day-to-day basis together with the increasing complexity of many of today’s societal challenges require an increasingly knowledgeable and adaptive citizenry and workforce. We face a global demand for new ways of continuously training and reskilling workers, and we need new socio-technical systems to better enable and advance human sensemaking, decision making, creative and analytical thinking, feeling, and doing. New techniques are needed that integrate artificial intelligence, machine learning and data-mining approaches in the service of augmenting human emotion, cognition, and behavior. The Center’s mission is to support progress through increased capacity, productivity, adaptability, participation, and access.

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Recent Papers

Hong, S., Hullman, J., and Bertini, E. Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs. ACM CSCW 2020. 

Kim, Y., Gergle, D., & Zhang, H. (2020). Opportunistic Supply Management: A Decision-theoretic Approach to Balance Helper Needs and System Efficiency in On-the-go Crowdsourcing. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI’20). 

Zacks, J. M., & Franconeri, S. L. (2020). Designing Graphs for Decision-Makers. Policy Insights from the Behavioral and Brain Sciences, 7(1), 52-63.

Diakopoulos, N. Automating the News: How Algorithms Are Rewriting the Media, 2019


Linguistic expression of preferences in social media for prediction and recommendation. LA Birnbaum, S O'banion, US Patent 10,685,181
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