On Generating Monolithic and Model Reconciling Explanations in Probabilistic Scenarios

The document proposes a novel framework for generating probabilistic monolithic and model reconciling explanations, leveraging probabilistic logic and algorithms based on the duality between minimal correction sets and minimal unsatisfiable sets to address the challenge of generating transparent and understandable explanations for AI decisions in uncertain environments.

Abstract

Explanation generation frameworks aim to make AI systems' decisions transparent and understandable to human users. However, generating explanations in uncertain environments characterized by incomplete information and probabilistic models remains a significant challenge. In this document, we propose a novel framework for generating probabilistic monolithic explanations and model reconciling explanations. Monolithic explanations provide self-contained reasons for an explanandum without considering the agent receiving the explanation, while model reconciling explanations account for the knowledge of the agent receiving the explanation. For monolithic explanations, our approach integrates uncertainty by utilizing probabilistic logic to increase the probability of the explanandum. For model reconciling explanations, we propose a framework that extends the logic-based variant of the model reconciliation problem to account for probabilistic human models, where the goal is to find explanations that increase the probability of the explanandum while minimizing conflicts between the explanation and the probabilistic human model. We introduce explanatory gain and explanatory power as quantitative metrics to assess the quality of these explanations. Further, we present algorithms that exploit the duality between minimal correction sets and minimal unsatisfiable sets to efficiently compute both types of explanations in probabilistic contexts. Extensive experimental evaluations on various benchmarks demonstrate the effectiveness and scalability of our approach in generating explanations under uncertainty.

Method

The document proposes a novel framework for generating probabilistic monolithic explanations and model reconciling explanations in uncertain environments characterized by incomplete information and probabilistic models. The framework integrates uncertainty using probabilistic logic and introduces the concepts of explanatory gain and explanatory power as quantitative metrics to evaluate the quality and effectiveness of the explanations. It also presents algorithms that leverage the duality between minimal correction sets and minimal unsatisfiable sets to efficiently compute both types of explanations in probabilistic contexts. The proposed approach aims to bridge the gap between classical explanation models and the inherent uncertainty in real-world scenarios, offering a better understanding of explanation quality. Additionally, the document provides extensive experimental evaluations to demonstrate the effectiveness and scalability of the proposed framework in generating explanations under uncertainty.

Main Finding

The main finding of this document is the proposal of a novel framework for generating probabilistic monolithic explanations and model reconciling explanations in uncertain environments characterized by incomplete information and probabilistic models. The framework integrates uncertainty using probabilistic logic and introduces the concepts of explanatory gain and explanatory power as quantitative metrics to evaluate the quality and effectiveness of the explanations. Additionally, the document presents algorithms that leverage the duality between minimal correction sets and minimal unsatisfiable sets to efficiently compute both types of explanations in probabilistic contexts. Experimental evaluations demonstrate the effectiveness and scalability of the proposed framework in generating explanations under uncertainty.

Conclusion

In conclusion, this document introduces a novel framework for generating probabilistic monolithic explanations and model reconciling explanations in uncertain environments characterized by incomplete information and probabilistic models. The framework leverages probabilistic logic and introduces the concepts of explanatory gain and explanatory power as quantitative metrics to assess the quality and effectiveness of the explanations. Additionally, the document presents algorithms that exploit the duality between minimal correction sets and minimal unsatisfiable sets to efficiently compute both types of explanations in probabilistic contexts. Experimental evaluations demonstrate the effectiveness and scalability of the proposed framework in generating explanations under uncertainty, highlighting its potential for real-world applications and its contribution to the field of explainable AI.

Keywords

Keywords for this document include: Monolithic explanations, Model reconciling explanations, Probabilistic scenarios, Explanatory gain, Explanatory power, Minimal correction sets, Minimal unsatisfiable sets, Probabilistic logic, Weighted maximum satisfiability, Knowledge base, Explanandum, Human belief base, Probabilistic model reconciling explanations, Minimal hitting set, Computational algorithms, Experimental evaluations.