From Facts to Foils: Designing and Evaluating Counterfactual Explanations for Smart Environments

2025-10-03

AI Summary

Rule-based intelligent environments lack mature methods for generating counterfactual explanations. Method: This paper introduces the first counterfactual explanation generation framework tailored to such environments, designed as a plug-in for existing explanation engines. It formally defines counterfactual reasoning logic under rule-based settings, constructs a generation mechanism integrating rule-based inference with causal-counterfactual hybrid logic, and proposes a context-dependent criterion for selecting explanation types. Contribution/Results: (1) It establishes a novel paradigm for actionable counterfactual explanations in rule-based intelligent environments; (2) A user study demonstrates that these explanations significantly outperform causal explanations in operational utility for problem-solving tasks, while also revealing strong contextual dependence in users’ explanation preferences.

Technology Category

• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality
• Machine Learning: Causal Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• 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

Abstract

Explainability is increasingly seen as an essential feature of rule-based smart environments. While counterfactual explanations, which describe what could have been done differently to achieve a desired outcome, are a powerful tool in eXplainable AI (XAI), no established methods exist for generating them in these rule-based domains. In this paper, we present the first formalization and implementation of counterfactual explanations tailored to this domain. It is implemented as a plugin that extends an existing explanation engine for smart environments. We conducted a user study (N=17) to evaluate our generated counterfactuals against traditional causal explanations. The results show that user preference is highly contextual: causal explanations are favored for their linguistic simplicity and in time-pressured situations, while counterfactuals are preferred for their actionable content, particularly when a user wants to resolve a problem. Our work contributes a practical framework for a new type of explanation in smart environments and provides empirical evidence to guide the choice of when each explanation type is most effective.

Problem

• Formalizing counterfactual explanations for rule-based smart environments
• Evaluating user preference between counterfactual and causal explanations
• Providing actionable explanations for resolving problems in smart systems

Innovation

• Formalized counterfactual explanations for rule-based systems
• Implemented as plugin extending existing explanation engine
• Evaluated user preference between causal and counterfactual explanations