Trust is a crucial factor affecting the adoption of machine learning (ML) models. Qualitative studies have revealed that end-users, particularly in the medical domain, need models that can express their uncertainty in decision-making allowing users to know when to ignore the model's recommendations. However, existing approaches for quantifying decision-making uncertainty are not model-agnostic, or they rely on complex statistical derivations that are not easily understood by laypersons or end-users, making them less useful for explaining the model's decision-making process. This work proposes a set of class-independent meta-heuristics that can characterize the complexity of an instance in terms of factors are mutually relevant to both human and ML decision-making. The measures are integrated into a meta-learning framework that estimates the risk of misclassification. The proposed framework outperformed predicted probabilities in identifying instances at risk of being misclassified. The proposed measures and framework hold promise for improving model development for more complex instances, as well as providing a new means of model abstention and explanation.

翻译：信任是影响机器学习模型采纳的关键因素。定性研究表明，终端用户（尤其是在医疗领域）需要能够在决策中表达不确定性的模型，以便用户知晓何时应忽略模型的建议。然而，现有的量化决策不确定性的方法要么不是模型无关的，要么依赖于复杂的统计推导，这些推导难以被外行或终端用户理解，从而在解释模型的决策过程方面实用性不足。本研究提出了一组与类别无关的元启发式方法，能够根据与人类和机器学习决策均相关的因素来描述实例的复杂度。这些度量被整合进一个元学习框架中，用于估计误分类风险。所提出的框架在识别可能被误分类的实例方面优于预测概率。所提出的度量与框架有望改进针对更复杂实例的模型开发，并提供一种新的模型弃权与解释方法。