通过反事实路径几何导航解释性多元宇宙
Navigating Explanatory Multiverse Through Counterfactual Path Geometry
AI总结：
本文提出“解释性多元宇宙”概念，用向量空间刻画所有可能反事实路径的几何属性，并设计机会潜力度量及图实现，使被解释者能依据路径特性选择反事实，在六个数据集上验证了其灵活性与有效性。
摘要：
反事实解释是解释（不透明）预测模型决策时的事实标准。其生成通常受到旨在最大化现实效用的技术约束和领域特定约束的限制。除了考虑与反事实实例本身相关的期望属性之外，保证存在一条将其与事实数据点连接起来的可行路径，近年来也变得越来越重要。虽然当前的可解释性方法确保这种旅程的步骤及其目的地符合所选约束，但它们忽略了这些反事实路径的多样性。为了解决这一不足，我们引入了“解释性多元宇宙”这一新概念，它涵盖了所有可能的反事实旅程。我们使用向量空间来定义它，并展示如何导航、推理和比较其中发现的反事实轨迹的几何性质。为此，我们概述了它们的空间属性——例如亲和性、分支、发散以及未来可能的收敛——并提出了一种一体化度量，称为机会潜力，用于量化这些属性。值得注意的是，我们的方法所提供的解释过程赋予了被解释者更多的自主权，使他们不仅可以根据绝对差异来选择反事实，还可以根据其连接路径的属性来选择。为了展示解释性多元宇宙在现实中的灵活性、益处和有效性，我们提出了其基于图的实现，并在六个表格和图像数据集上进行了定性和定量评估。
英文摘要：
Counterfactual explanations are the de facto standard when tasked with interpreting decisions of (opaque) predictive models. Their generation is often subject to technical and domain-specific constraints that aim to maximise their real-life utility. In addition to considering desiderata pertaining to the counterfactual instance itself, guaranteeing existence of a viable path connecting it with the factual data point has recently gained relevance. While current explainability approaches ensure that the steps of such a journey as well as its destination adhere to selected constraints, they neglect the multiplicity of these counterfactual paths. To address this shortcoming we introduce the novel concept of explanatory multiverse that encompasses all the possible counterfactual journeys. We define it using vector spaces, showing how to navigate, reason about and compare the geometry of counterfactual trajectories found within it. To this end, we overview their spatial properties -- such as affinity, branching, divergence and possible future convergence -- and propose an all-in-one metric, called opportunity potential, to quantify them. Notably, the explanatory process offered by our method grants explainees more agency by allowing them to select counterfactuals not only based on their absolute differences but also according to the properties of their connecting paths. To demonstrate real-life flexibility, benefit and efficacy of explanatory multiverse we propose its graph-based implementation, which we use for qualitative and quantitative evaluation on six tabular and image data sets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
