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Recently, there has been a proliferation of personal health applications describing to use Artificial Intelligence (AI) to assist health consumers in making health decisions based on their data and algorithmic outputs. However, it is still unclear how such descriptions influence individuals' perceptions of such apps and their recommendations. We therefore investigate how current AI descriptions influence individuals' attitudes towards algorithmic recommendations in fertility self-tracking through a simulated study using three versions of a fertility app. We found that participants preferred AI descriptions with explanation, which they perceived as more accurate and trustworthy. Nevertheless, they were unwilling to rely on these apps for high-stakes goals because of the potential consequences of a failure. We then discuss the importance of health goals for AI acceptance, how literacy and assumptions influence perceptions of AI descriptions and explanations, and the limitations of transparency in the context of algorithmic decision-making for personal health. 

HCI researchers increasingly conduct emotionally demanding research in a variety of different contexts. Though scholarship has begun to address the experiences of HCI researchers conducting this work, there is a need to develop guidelines and best practices for researcher wellbeing. In this one-day CHI workshop, we will bring together a group of HCI researchers across sectors and career levels who conduct emotionally demanding research to discuss their experiences, self-care practices, and strategies for research. Based on these discussions, we will work with workshop attendees to develop best practices and guidelines for researcher wellbeing in the context of emotionally demanding HCI research; launch a repository of community-sourced resources for researcher wellbeing; document the experiences of HCI researchers conducting emotionally demanding research; and establish a community of HCI researchers conducting this type of work.