  Machine learning systems can help humans to make decisions by providing
decision suggestions (i.e., a label for a datapoint). However, individual
datapoints do not always provide enough clear evidence to make confident
suggestions. Although methods exist that enable systems to identify those
datapoints and subsequently abstain from suggesting a label, it remains unclear
how users would react to such system behavior. This paper presents first
findings from a user study on systems that do or do not abstain from labeling
ambiguous datapoints. Our results show that label suggestions on ambiguous
datapoints bear a high risk of unconsciously influencing the users' decisions,
even toward incorrect ones. Furthermore, participants perceived a system that
abstains from labeling uncertain datapoints as equally competent and
trustworthy as a system that delivers label suggestions for all datapoints.
Consequently, if abstaining does not impair a system's credibility, it can be a
useful mechanism to increase decision quality.
