An estimated 95% of our scientific knowledge about people, their behavior, perception, and preferences is based on studies with “WEIRD” samples, an acronym for participants who are Western, Educated, Industrialized, Rich, and Democratic. My work in Human-Computer Interaction has shown that technology developed for WEIRD users, based on knowledge that is derived from studies with mostly US-based student populations, is often misaligned with the preferences, behaviors, and abilities of a large proportion of the world’s population: Users differ in how they perceive information, what they find appealing, and what they can work with most efficiently.
A possible solution to the conventional “one size fits all” approach is to predict what a user likes and works with most efficiently, and to leverage this knowledge to design user interfaces accordingly. In this talk, I will show how the online experiment platform LabintheWild (www.labinthewild.org) has enabled me to conduct large-scale behavioral experiments needed to build such predictive models. LabintheWild enables participants to compare themselves to others in exchange for study participation; a feedback mechanism that has attracted an average of 1,000 participants a day from more than 200 countries and various demographic groups. I show how LabintheWild experiments have enabled me to study less WEIRD users than possible in conventional laboratory studies, and how the results of these studies allowed me to build systems that predict what users like.
Katharina Reinecke is an assistant professor in the Computer Science & Engineering department at the University of Washington. Prior to that, she was an assistant professor at the University of Michigan School of Information and the Department of Computer Science. She received her Ph.D. in Computer Science from the University of Zurich, Switzerland, and spent her postdoctoral years at the Harvard School of Engineering and Applied Sciences. Her research in human-computer interaction focuses on understanding how culture affects users’ perception, behavior, and preferences when interacting with technology. Using data from her online experiment platform LabintheWild.org, she builds systems that predict the suitability of user interfaces for people around the world. Katharina has received Best Paper awards and nominations at premier venues in human-computer interaction (ACM CHI, ACM CSCW, UMAP), a Google Research Faculty award, as well as the Mercator Prize for the best doctoral thesis at the University of Zurich in 2011.