找到树模型决策的最通用解释，覆盖范围更广且保持正确性。
Most General Explanations of Tree Ensembles (Extended Version)
- 基于形式化模型寻找覆盖最大输入空间的解释区间
- 相比传统解释，新方法能覆盖更多输入组合且预测一致
- 适合需要可解释性的人工智能决策场景
可解释人工智能（XAI）对建立对AI系统运行的信任至关重要。一个核心问题是：为何作出此决策？形式化XAI方法通过模型识别反事实解释。尽管反事实解释适用于具有相同具体值的大量输入，但对数值型输入而言，更通用的解释更受青睐。所谓的膨胀反事实解释为每个特征给出区间，确保落入该区间的任意输入仍产生相同预测。膨胀解释覆盖了更大的输入空间，因此被视为更通用。然而，对单一实例可能存在多个（膨胀）反事实解释。哪个最优？本文提出如何找到树集成模型决策的最通用反事实解释——在保证正确性的前提下，覆盖尽可能大的输入空间。由于我们只需为一次决策提供一个解释，最通用解释具有最广泛的适用性，也最可能被人类视为合理。论文已被IJCAI2025接收。
原文摘要 · Abstract (English)
Explainable Artificial Intelligence (XAI) is critical for attaining trust in the operation of AI systems. A key question of an AI system is ``why was this decision made this way''. Formal approaches to XAI use a formal model of the AI system to identify abductive explanations. While abductive explanations may be applicable to a large number of inputs sharing the same concrete values, more general explanations may be preferred for numeric inputs. So-called inflated abductive explanations give intervals for each feature ensuring that any input whose values fall withing these intervals is still guaranteed to make the same prediction. Inflated explanations cover a larger portion of the input space, and hence are deemed more general explanations. But there can be many (inflated) abductive explanations for an instance. Which is the best? In this paper, we show how to find a most general abductive explanation for an AI decision. This explanation covers as much of the input space as possible, while still being a correct formal explanation of the model's behaviour. Given that we only want to give a human one explanation for a decision, the most general explanation gives us the explanation with the broadest applicability, and hence the one most likely to seem sensible. (The paper has been accepted at IJCAI2025 conference.)
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