Objectives: Hand hygiene has long been promoted as the most effective way to prevent the transmission of infection. However, due to low compliance and low quality of hand hygiene reported in previous studies, constant monitoring of hand hygiene compliance and quality among healthcare workers is crucial. This study investigated the feasibility of using a thermal camera with an RGB camera to detect hand coverage of alcohol-based formulation, thereby monitoring the quality of hand rubbing. Methods: In total, 32 participants were recruited to participate in this study. Participants were required to perform four types of hand rubbing to achieve different coverage of the alcohol-based formulation. After each task, participants' hands were photographed under a thermal camera and an RGB camera, while an ultraviolet (UV) test was used to provide the ground truth of hand coverage of alcohol-based formulation. U-Net was used to segment areas exposed to alcohol-based formulation from thermal images, and system performance was evaluated by comparing differences in coverage between thermal images and UV images in terms of accuracy and Dice coefficient. Results: This system found promising results in terms of accuracy (93.5%) and Dice coefficient (87.1%) when observations took place 10 s after hand rubbing. At 60 s after hand rubbing, accuracy and Dice coefficient were 92.4% and 85.7%. Conclusions: Thermal imaging has potential for accurate, constant and systematic monitoring of the quality of hand hygiene.

AI systems are increasingly incorporated into human decision-making. Yet, human decision-makers are often affected by their cognitive biases. In critical settings, such as medical diagnosis, criminal judgment, or information consumption, these cognitive biases hinder optimal decision outcomes, thereby resulting in dangerous decisions and negative societal impact. The use of AI systems can amplify and exacerbate cognitive biases in their users. In this workshop, we seek to foster discussions on ongoing research around cognitive biases in human-AI collaboration and identify future research directions to understand, quantify, and mitigate the effects of cognitive biases. We will explore cognitive biases appearing in various contexts of human-AI collaboration: what can cause them?; how can we measure, model, mitigate, and manage cognitive biases?; and how can we utilise cognitive biases for the greater good? We will reflect on workshop discussions to form a research community around cognitive biases and bias-aware systems.