Good mental health is imperative for one’s wellbeing. While clinical mental disorder treatments exist, self-care is an essential aspect of mental health. This paper explores the use and perceived trust of conversational agents, chatbots, in the context of crowdsourced self-care through a between-subjects study (N = 80). One group used a standalone system with a conventional web interface to discover self-care methods. The other group used the same system wrapped in a chatbot interface, facilitating utterances and turn-taking between the user and a chatbot. We identify the security and integrity of the systems as critical factors that affect users’ trust. The chatbot interface scored lower on both these factors, and we contemplate the potential underlying reasons for this. We complement the quantitative data with qualitative analysis and synthesize our findings to identify suggestions for using chatbots in mental health contexts.

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.