  Despite AI's superhuman performance in a variety of domains, humans are often
unwilling to adopt AI systems. The lack of interpretability inherent in many
modern AI techniques is believed to be hurting their adoption, as users may not
trust systems whose decision processes they do not understand. We investigate
this proposition with a novel experiment in which we use an interactive
prediction task to analyze the impact of interpretability and outcome feedback
on trust in AI and on human performance in AI-assisted prediction tasks. We
find that interpretability led to no robust improvements in trust, while
outcome feedback had a significantly greater and more reliable effect. However,
both factors had modest effects on participants' task performance. Our findings
suggest that (1) factors receiving significant attention, such as
interpretability, may be less effective at increasing trust than factors like
outcome feedback, and (2) augmenting human performance via AI systems may not
be a simple matter of increasing trust in AI, as increased trust is not always
associated with equally sizable improvements in performance. These findings
invite the research community to focus not only on methods for generating
interpretations but also on techniques for ensuring that interpretations impact
trust and performance in practice.
