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.

翻译：机器学习系统可通过提供决策建议（即数据点的标签）辅助人类决策。然而，并非所有数据点都能提供足够清晰的证据以形成可靠建议。尽管现有方法能使系统识别这些数据点并放弃给出标签建议，但用户对此类系统行为的反应仍不明确。本文首次报告了关于标注或不标注模糊数据点的系统用户研究结果。研究发现，对模糊数据点的标签建议存在高风险——可能无意识地影响用户决策，甚至使其偏向错误结论。此外，参与者认为对不确定数据点选择弃权的系统与对所有数据点均提供标签建议的系统具有同等的胜任力和可信度。因此，若弃权机制不损害系统可信度，则其可作为提升决策质量的有效手段。