  In many real world contexts, successful human-AI collaboration requires
humans to productively integrate complementary sources of information into
AI-informed decisions. However, in practice human decision-makers often lack
understanding of what information an AI model has access to in relation to
themselves. There are few available guidelines regarding how to effectively
communicate about unobservables: features that may influence the outcome, but
which are unavailable to the model. In this work, we conducted an online
experiment to understand whether and how explicitly communicating potentially
relevant unobservables influences how people integrate model outputs and
unobservables when making predictions. Our findings indicate that presenting
prompts about unobservables can change how humans integrate model outputs and
unobservables, but do not necessarily lead to improved performance.
Furthermore, the impacts of these prompts can vary depending on
decision-makers' prior domain expertise. We conclude by discussing implications
for future research and design of AI-based decision support tools.
