On Logic-Based Explainability with Partially Specified Inputs
This addresses the challenge of explainability in real-world deployments where missing data is common, though it is incremental as it extends prior methods rather than introducing a new paradigm.
The paper tackles the problem of computing logic-based explanations for machine learning predictions when inputs are partially specified due to missing data, showing that existing algorithms can be generalized for this purpose without increasing computational complexity.
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.