Reasoning about Counterfactuals and Explanations: Problems, Results and Directions
This work addresses the need for interpretable and explainable AI systems, particularly in classification tasks, but appears incremental as it builds on existing answer-set programming methods.
The paper tackles the problem of specifying counterfactual interventions and computing responsibility-based explanations for classification results using answer-set programming, achieving flexible and modular reasoning with seamless integration of domain knowledge.
There are some recent approaches and results about the use of answer-set programming for specifying counterfactual interventions on entities under classification, and reasoning about them. These approaches are flexible and modular in that they allow the seamless addition of domain knowledge. Reasoning is enabled by query answering from the answer-set program. The programs can be used to specify and compute responsibility-based numerical scores as attributive explanations for classification results.