'A magic ingredient, a secret spice, a special blend, for it can all be nice!': The Human Quotient for Better AI Systems

The unprecedented rise in the adoption of artificial intelligence techniques and automation in many contexts is concomitant with the shortcomings of such technology concerning robustness, interpretability, usability, trustworthiness, and explainability. Crowd computing offers a viable means to leverage human intelligence at scale for data creation, enrichment, and interpretation, demonstrating a great potential to improve the performance of AI systems and increase AI adoption in general. Can we help pave a future where humans can benefit by working seamlessly with AI? What can we do to facilitate appropriate trust and reliance of people on AI systems? This talk will discuss the intriguing and pertinent role of human input in propelling better AI technology in the quickly evolving age of generative models.

Speaker

Ujwal Gadiraju is an Assistant Professor at the Web Information Systems (WIS) group of the Software Technology department at Delft University of Technology, the Netherlands. He is a Director of the Delft AI ‘Design@Scale’ Lab, which focuses on Human-AI collaboration to design solutions for complex social problems. Ujwal leads a research line on Crowd Computing and Human-Centered AI at the WIS group. He is an ACM Distinguished Speaker. Before joining the WIS group, Ujwal worked at the L3S Research Center as a Postdoctoral researcher between 2017-2020. He received a PhD (Dr. rer. nat.) in Computer Science from the Leibniz University of Hannover, Germany, in 2017 and an MSc. Computer Science degree from TU Delft, the Netherlands, in 2012. His research interests lie at the intersection of Artificial Intelligence (AI) and Human-Computer Interaction (HCI). He has published over 135 peer-reviewed articles, including at premier venues such as ACM CHI, ACM CSCW, ACM TOCHI, AAAI HCOMP, ACM TheWebConf, ACM SIGIR, ACM UBICOMP, ACM CIKM, ACM WSDM, ACM HT, ACM UMAP, among others. His work has been recognized with various honors, including best paper awards at top-tier HCI and AI conferences. Ujwal’s prior work in Crowd Computing has explored methods to improve the effectiveness of the crowdsourcing paradigm, running large-scale human-centered experiments to understand the interaction between humans and machines and the societal impact of algorithmic decision-making. Ujwal’s goal is to create novel methods, interfaces, systems, and tools to overcome existing challenges on our path towards building better AI systems and facilitating better reliance of humans on AI systems. For more information about his research and other activities, please visit https://ujwalgadiraju.com.