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arXiv · 2104.06982

To Trust or Not to Trust a Regressor: Estimating and Explaining Trustworthiness of Regression Predictions

Kim de Bie·Ana Lucic·Hinda Haned
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Abstract

In hybrid human-AI systems, users need to decide whether or not to trust an algorithmic prediction while the true error in the prediction is unknown. To accommodate such settings, we introduce RETRO-VIZ, a method for (i) estimating and (ii) explaining trustworthiness of regression predictions. It consists of RETRO, a quantitative estimate of the trustworthiness of a prediction, and VIZ, a visual explanation that helps users identify the reasons for the (lack of) trustworthiness of a prediction. We find that RETRO-scores negatively correlate with prediction error across 117 experimental settings, indicating that RETRO provides a useful measure to distinguish trustworthy predictions from untrustworthy ones. In a user study with 41 participants, we find that VIZ-explanations help users identify whether a prediction is trustworthy or not: on average, 95.1% of participants correctly select the more trustworthy prediction, given a pair of predictions. In addition, an average of 75.6% of participants can accurately describe why a prediction seems to be (not) trustworthy. Finally, we find that the vast majority of users subjectively experience RETRO-VIZ as a useful tool to assess the trustworthiness of algorithmic predictions.

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Kim de Bie, Ana Lucic, Hinda Haned. 2021-07-28. To Trust or Not to Trust a Regressor: Estimating and Explaining Trustworthiness of Regression Predictions. https://arxiv.org/abs/2104.06982

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