  Machine Translation (MT) has the potential to help people overcome language
barriers and is widely used in high-stakes scenarios, such as in hospitals.
However, in order to use MT reliably and safely, users need to understand when
to trust MT outputs and how to assess the quality of often imperfect
translation results. In this paper, we discuss research directions to support
users to calibrate trust in MT systems. We share findings from an empirical
study in which we conducted semi-structured interviews with 20 clinicians to
understand how they communicate with patients across language barriers, and if
and how they use MT systems. Based on our findings, we advocate for empirical
research on how MT systems are used in practice as an important first step to
addressing the challenges in building appropriate trust between users and MT
tools.
