On Generating Monolithic and Model Reconciling Explanations in Probabilistic Scenarios

2024-05-29

arXiv.org

AI Summary

Existing explainable AI (XAI) methods lack probabilistic explanation mechanisms that jointly ensure transparency and user adaptability under uncertainty. Method: We propose the first unified framework generating two complementary types of probabilistic explanations: (1) self-contained, user-agnostic singleton explanations; and (2) model-harmonized explanations grounded in users’ probabilistic mental models. Our approach innovatively extends model harmonization to probabilistic human models, introduces the novel quantitative metrics “explanation gain” and “explanatory power”, and leverages MUS/MCS duality for efficient computation. The framework integrates probabilistic logic programming, Bayesian knowledge representation, and logic-based harmonization techniques. Results: Extensive experiments across diverse benchmarks demonstrate significant improvements in explanation credibility, user adaptability, and computational efficiency—validating both the effectiveness and scalability of the framework.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Reasoning under Uncertainty: Probabilistic Programming
• Machine Learning: Probabilistic Circuits and Graphical Models

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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 paper, 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.

Problem

• Generating explanations in uncertain probabilistic environments
• Addressing monolithic and model reconciling explanation types
• Minimizing conflicts between explanations and human models

Innovation

• Probabilistic logic for monolithic explanations
• Model reconciliation with probabilistic human models
• Algorithms using minimal correction sets duality