Explainability and uncertainty quantification are two pillars of trustable artificial intelligence. However, the reasoning behind uncertainty estimates is generally left unexplained. Identifying the drivers of uncertainty complements explanations of point predictions in recognizing potential model limitations. It facilitates the detection of oversimplification in the uncertainty estimation process. Explanations of uncertainty enhance communication and trust in decisions. They allow for verifying whether the main drivers of model uncertainty are relevant and may impact model usage. So far, the subject of explaining uncertainties has been rarely studied. The few exceptions in existing literature are tailored to Bayesian neural networks or rely heavily on technically intricate approaches, hindering their broad adoption. We propose variance feature attribution, a simple and scalable solution to explain predictive aleatoric uncertainties. First, we estimate uncertainty as predictive variance by equipping a neural network with a Gaussian output distribution by adding a variance output neuron. Thereby, we can rely on pre-trained point prediction models and fine-tune them for meaningful variance estimation. Second, we apply out-of-the-box explainers on the variance output of these models to explain the uncertainty estimation. We evaluate our approach in a synthetic setting where the data-generating process is known. We show that our method can explain uncertainty influences more reliably and faster than the established baseline CLUE. We fine-tune a state-of-the-art age regression model to estimate uncertainty and obtain attributions. Our explanations highlight potential sources of uncertainty, such as laugh lines. Variance feature attribution provides accurate explanations for uncertainty estimates with little modifications to the model architecture and low computational overhead.

翻译：可解释性与不确定性量化是可信任人工智能的两大支柱。然而，不确定性估计背后的推理通常未被解释。识别不确定性的驱动因素能够补充点预测的解释，帮助识别潜在模型局限性，并促进检测不确定性估计过程中的过度简化。不确定性解释能增强决策沟通与信任，便于验证模型不确定性的主要驱动因素是否相关及其对模型使用的影响。目前，不确定性解释研究尚属罕见，现有文献中的少数例外方法或局限于贝叶斯神经网络，或过度依赖技术复杂的途径，阻碍了其广泛采用。本文提出方差特征归因这一简洁且可扩展的解决方案，用于解释预测偶然不确定性。首先，通过为神经网络添加方差输出神经元以配备高斯输出分布，将不确定性估计为预测方差。由此，我们可基于预训练的点预测模型，通过微调实现有意义的方差估计。其次，我们对这些模型的方差输出应用现成的解释器，以解释不确定性估计。我们在已知数据生成过程的合成场景中评估该方法，证明其能比现有基准方法CLUE更可靠、更快速地解释不确定性影响。我们微调了最先进的年龄回归模型以估计不确定性并获取归因结果。解释结果突显了潜在不确定性来源（如笑纹）。方差特征归因能在极小修改模型架构且计算开销较低的前提下，为不确定性估计提供精确解释。