Misplaced Trust: Measuring the Interference of Machine Learning in Human Decision-Making

ML decision-aid systems are increasingly common on the web, but their successful integration relies on people trusting them appropriately: they should use the system to fill in gaps in their ability, but recognize signals that the system might be incorrect. We measured how people's trust in ML recommendations differs by expertise and with more system information through a task-based study of 175 adults. We used two tasks that are difficult for humans: comparing large crowd sizes and identifying similar-looking animals. Our results provide three key insights: (1) People trust incorrect ML recommendations for tasks that they perform correctly the majority of the time, even if they have high prior knowledge about ML or are given information indicating the system is not confident in its prediction; (2) Four different types of system information all increased people's trust in recommendations; and (3) Math and logic skills may be as important as ML for decision-makers working with ML recommendations.Comment: 10 page

AI and Literacy for Non-Technical Students: A Hybrid-Augmented Learning Factory

Artificial Intelligence (AI) has made a strong impact on business and private life. Nonetheless, understanding how AI works and which role data specifically plays in this context remain unclear for many people. We argue that especially students with non-technical backgrounds should build up AI & data literacy to understand the key concepts and leverage their potential in their field of study and research. For this purpose, we present the concept of a hybrid-augmented learning factory, where students can explore AI & data concepts with interactive and immersive technologies in a physical and virtual environment. In this workshop paper, we demonstrate our overarching idea of the hybrid-augmented learning factory as well as our current progress on implementing learning applications for the learning factory