DeepAI AI Chat
Log In Sign Up

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

by   Harini Suresh, et al.

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.


page 5

page 7


How Fake News Affect Trust in the Output of a Machine Learning System for News Curation

People are increasingly consuming news curated by machine learning (ML) ...

FoundWright: A System to Help People Re-find Pages from Their Web-history

Re-finding information is an essential activity, however, it can be diff...

Declarative Machine Learning Systems

In the last years machine learning (ML) has moved from a academic endeav...

Human-Centered Tools for Coping with Imperfect Algorithms during Medical Decision-Making

Machine learning (ML) is increasingly being used in image retrieval syst...

Trust in Prediction Models: a Mixed-Methods Pilot Study on the Impact of Domain Expertise

People's trust in prediction models can be affected by many factors, inc...

Stability of Weighted Majority Voting under Estimated Weights

Weighted Majority Voting (WMV) is a well-known optimal decision rule for...