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

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.

Author Information

Kim de Bie (University of Amsterdam)
Ana Lucic (Partnership on AI, University of Amsterdam)

Research fellow at the Partnership on AI and PhD student at the University of Amsterdam, working primarily on explainable ML.

Hinda Haned (University of Amsterdam)

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