Before revision:  We aim to explore the impact of contrastive explanations about large errors on users' attitudes towards a ' black-box' model. In this work, we make two contributions. The first is an algorithm, Monte Carlo Bounds for Reasonable Predictions (MC-BRP) . Given a large error, MC-BRP determines (1) feature values that would result in a reasonable prediction, and (2) general trends between each feature and the target,  based on Monte Carlo simulations.
After revision:  We aim to explore the impact of contrastive explanations about large errors on users' attitudes towards a ' black-box' " model. We propose an algorithm, Monte Carlo Bounds for Reasonable Predictions (MC-BRP) . Given a large error, MC-BRP determines (1) feature values that would result in a reasonable prediction, and (2) general trends between each feature and the target, based on Monte Carlo simulations.