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.
Meet Our Faculty Experts
- Larry Birnbaum
- Nick Diakopoulos
- Matthew Easterday
- Steve Franconeri
- Elizabeth Gerber
- Jessica Hullman
- Michael Horn
- Duri Long
- Nell O'Rourke
- Bryan Pardo
- Marcelo Worsley
- Haoqi Zhang
Example Projects
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