In the practical deployment of machine learning (ML) models, missing data represents a recurring challenge. Missing data is often addressed when training ML models. But missing data also needs to be addressed when deciding predictions and when explaining those predictions. Missing data represents an opportunity to partially specify the inputs of the prediction to be explained. This paper studies the computation of logic-based explanations in the presence of partially specified inputs. The paper shows that most of the algorithms proposed in recent years for computing logic-based explanations can be generalized for computing explanations given the partially specified inputs. One related result is that the complexity of computing logic-based explanations remains unchanged. A similar result is proved in the case of logic-based explainability subject to input constraints. Furthermore, the proposed solution for computing explanations given partially specified inputs is applied to classifiers obtained from well-known public datasets, thereby illustrating a number of novel explainability use cases.

翻译：在机器学习模型的实际部署过程中，数据缺失是一种常见挑战。训练机器学习模型时通常需要处理缺失数据，但在进行预测以及解释这些预测时，同样需要解决数据缺失问题。缺失数据为部分指定待解释预测的输入提供了可能性。本文研究了在部分指定输入条件下计算逻辑解释的问题。研究表明，近年来提出的多数逻辑解释计算算法可推广至部分指定输入场景下的解释计算。一个相关结论是，逻辑解释的计算复杂度保持不变。在输入约束条件下的逻辑解释性中也证明了类似结果。此外，本文提出的部分指定输入解释计算方法被应用于公开数据集的分类器，从而展示了若干新型解释性用例。