Increasing volumes of data allow environmental scientists to use machine learning to construct data-driven models of phenomena. These models can provide decision-relevant predictions, but confident decision-making requires that the involved uncertainties are understood. We argue that existing frameworks for characterizing uncertainties are not appropriate for data-driven models because of their focus on distinct locations of uncertainty. We propose a framework for uncertainty assessment that uses argument analysis to assess the justification of the assumption that the model is fit for the predictive purpose at hand. Its flexibility makes the framework applicable to data-driven models. The framework is illustrated using a case study from environmental science. We show that data-driven models can be subject to substantial second-order uncertainty, i.e., uncertainty in the assessment of the predictive uncertainty, because they are often applied to ill-understood problems. We close by discussing the implications of the predictive uncertainties of data-driven models for decision-making.

数据量的增加使环境科学家可以使用机器学习来构建数据驱动的现象模型。这些模型可以提供与决策相关的预测，但是自信的决策需要了解所涉及的不确定性。我们认为，用于表征不确定性的现有框架不适用于数据驱动的模型，因为它们专注于不确定性的不同位置。我们提出了不确定性评估的框架，该框架使用参数分析来评估该模型适合手头预测目的的假设的合理性。它的灵活性使该框架适用于数据驱动的模型。使用环境科学案例研究说明了该框架。我们表明，数据驱动的模型可能会遭受大量的二阶不确定性，即 e。不确定性在评估预测性不确定性中的原因，因为它们通常应用于无法理解的问题。最后，我们讨论数据驱动模型的预测不确定性对决策的影响。