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

Misplaced Trust: Measuring the Interference of Machine Learning in Human Decision-Making