ML decision-aid systems are increasingly common on the web, but their
successful integration relies on people trusting them appropriately: they
should use the system to fill in gaps in their ability, but recognize signals
that the system might be incorrect. We measured how people's trust in ML
recommendations differs by expertise and with more system information through a
task-based study of 175 adults. We used two tasks that are difficult for
humans: comparing large crowd sizes and identifying similar-looking animals.
Our results provide three key insights: (1) People trust incorrect ML
recommendations for tasks that they perform correctly the majority of the time,
even if they have high prior knowledge about ML or are given information
indicating the system is not confident in its prediction; (2) Four different
types of system information all increased people's trust in recommendations;
and (3) Math and logic skills may be as important as ML for decision-makers
working with ML recommendations.

研究人员探究了人们在处理两项困难任务时，对机器学习（ML）建议的信任程度；他们发现，即使人们了解这些任务，并给出了表明该系统不自信的信息，但在执行大部分时间正确的任务时，人们也会相信错误的 ML 建议，并且提供了四种不同类型的系统信息可增加人们对建议的信任，并且数学和逻辑技能对于使用 ML 建议的决策者来说可能与 ML 一样重要。