分析可解释机器学习模型的解释复杂度，揭示不同模型的解释难易程度。
Explaining Decisions in ML Models: a Parameterized Complexity Analysis (Part I)
- 从参数化复杂度角度研究决策树、集成模型等的解释机制。
- 发现局部与全局解释在不同模型中存在显著复杂度差异。
- 为可解释AI研究提供理论基础，适合关注AI透明性的研究者。
本文对多种机器学习模型中的解释问题进行了系统的理论分析，聚焦于具有透明内部机制的模型，而非常见的黑箱模型。研究涵盖两类核心解释问题：溯因式解释与对比式解释，每类均考虑局部与全局变体。所涉模型包括决策树、决策集、决策列表、布尔电路及其集成形式，各类模型带来独特的解释挑战。该工作填补了可解释人工智能（XAI）领域在解释复杂性理论方面的空白，为理解这些模型生成解释的计算难度提供了基础洞见，对推动可解释性研究及强化人工智能系统透明性与问责性具有重要意义。
原文摘要 · Abstract (English)
This paper presents a comprehensive theoretical investigation into the parameterized complexity of explanation problems in various machine learning (ML) models. Contrary to the prevalent black-box perception, our study focuses on models with transparent internal mechanisms. We address two principal types of explanation problems: abductive and contrastive, both in their local and global variants. Our analysis encompasses diverse ML models, including Decision Trees, Decision Sets, Decision Lists, Boolean Circuits, and ensembles thereof, each offering unique explanatory challenges. This research fills a significant gap in explainable AI (XAI) by providing a foundational understanding of the complexities of generating explanations for these models. This work provides insights vital for further research in the domain of XAI, contributing to the broader discourse on the necessity of transparency and accountability in AI systems.
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