Explainable AI in Time-Sensitive Scenarios: Prefetched Offline Explanation Model
This work addresses the problem of providing quick and effective AI explanations for users in time-sensitive applications, representing an incremental improvement over existing methods.
The paper tackles the need for fast, trustworthy AI explanations in time-sensitive scenarios by introducing Poem, a model-agnostic algorithm for image data that generates exemplars, counterexemplars, and saliency maps. It shows that Poem outperforms its predecessor Abele in speed and the quality of generated explanations, though specific numerical gains are not detailed.
As predictive machine learning models become increasingly adopted and advanced, their role has evolved from merely predicting outcomes to actively shaping them. This evolution has underscored the importance of Trustworthy AI, highlighting the necessity to extend our focus beyond mere accuracy and toward a comprehensive understanding of these models' behaviors within the specific contexts of their applications. To further progress in explainability, we introduce Poem, Prefetched Offline Explanation Model, a model-agnostic, local explainability algorithm for image data. The algorithm generates exemplars, counterexemplars and saliency maps to provide quick and effective explanations suitable for time-sensitive scenarios. Leveraging an existing local algorithm, \poem{} infers factual and counterfactual rules from data to create illustrative examples and opposite scenarios with an enhanced stability by design. A novel mechanism then matches incoming test points with an explanation base and produces diverse exemplars, informative saliency maps and believable counterexemplars. Experimental results indicate that Poem outperforms its predecessor Abele in speed and ability to generate more nuanced and varied exemplars alongside more insightful saliency maps and valuable counterexemplars.