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As machine learning (ML) models are increasingly being employed to assisthuman decision makers, it becomes critical to provide these decision makerswith relevant inputs which can help them decide if and how to incorporate modelpredictions into their decision making. For instance, communicating theuncertainty associated with model predictions could potentially be helpful inthis regard. In this work, we carry out user studies (1,330 responses from 190participants) to systematically assess how people with differing levels ofexpertise respond to different types of predictive uncertainty (i.e., posteriorpredictive distributions with different shapes and variances) in the context ofML assisted decision making for predicting apartment rental prices. We foundthat showing posterior predictive distributions led to smaller disagreementswith the ML model's predictions, regardless of the shapes and variances of theposterior predictive distributions we considered, and that these effects may besensitive to expertise in both ML and the domain. This suggests that posteriorpredictive distributions can potentially serve as useful decision aids whichshould be used with caution and take into account the type of distribution andthe expertise of the human.

When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making