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Workshop: ICML Workshop on Human in the Loop Learning (HILL)

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

Kim de Bie · Ana Lucic · Hinda Haned


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. In a user study with 41 participants, we confirm that RETRO-VIZ helps users identify whether and why a prediction is trustworthy or not.

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