Score-Based Explanations in Data Management and Machine Learning
This work addresses the need for interpretable explanations in data systems and ML models, but it appears incremental as it describes existing approaches rather than introducing new methods.
The paper tackles the problem of explaining outcomes in data management and machine learning by assigning numerical scores to inputs, focusing on query answers in databases and classification results, and argues for incorporating domain knowledge into these score computations.
We describe some approaches to explanations for observed outcomes in data management and machine learning. They are based on the assignment of numerical scores to predefined and potentially relevant inputs. More specifically, we consider explanations for query answers in databases, and for results from classification models. The described approaches are mostly of a causal and counterfactual nature. We argue for the need to bring domain and semantic knowledge into score computations; and suggest some ways to do this.