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.

RETRO-VIZ是一种可视化方法，通过RETRO的定量估计和VIZ的可视化解释，帮助用户识别算法预测的可信度，实验证明RETRO可用于区分可信和不可信的预测，在用户研究中，VIZ解释有助于用户正确选择更可信的预测。

对回归器的信任：回归预测的可信度估计和解释