We address a relatively under-explored aspect of human-computer interaction: people\u2019s abilities to understand the relationship between a machine learning model\u2019s stated performance on held-out data and its expected performance post-deployment. We conduct large-scale, randomized human subject experiments to examine whether laypeople\u2019s trust in a model, measured in terms of both the frequency with which they revise their predictions to match those of the model and their self-reported levels of trust in the model, varies depending on the model\u2019s stated accuracy on held-out data and on its observed accuracy in practice. We find that people\u2019s trust in a model is affected by both its stated accuracy and its observed accuracy and that the effect of stated accuracy can change depending on the observed accuracy. Our work relates to recent research on interpretable machine learning, but moves beyond the typical focus on model internals, exploring a different component of the machine learning pipeline.<\/p>\n","protected":false},"excerpt":{"rendered":"
We address a relatively under-explored aspect of human-computer interaction: people\u2019s abilities to understand the relationship between a machine learning model\u2019s stated performance on held-out data and its expected performance post-deployment. We conduct large-scale, randomized human subject experiments to examine whether laypeople\u2019s trust in a model, measured in terms of both the frequency with which they 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