Recently, counterfactuals using "if-only" explanations have become very
popular in eXplainable AI (XAI), as they describe which changes to
feature-inputs of a black-box AI system result in changes to a (usually
negative) decision-outcome. Even more recently, semi-factuals using "even-if"
explanations have gained more attention. They elucidate the feature-input
changes that do \textit{not} change the decision-outcome of the AI system, with
a potential to suggest more beneficial recourses. Some semi-factual methods use
counterfactuals to the query-instance to guide semi-factual production
(so-called counterfactual-guided methods), whereas others do not (so-called
counterfactual-free methods). In this work, we perform comprehensive tests of 8
semi-factual methods on 7 datasets using 5 key metrics, to determine whether
counterfactual guidance is necessary to find the best semi-factuals. The
results of these tests suggests not, but rather that computing other aspects of
the decision space lead to better semi-factual XAI.

通过在 7 个数据集上使用 5 个关键指标对 8 种半事实方法进行全面测试，确定是否需要使用反事实指导来找到最佳的半事实解释，结果表明不需要，而是计算决策空间的其他方面能够得到更好的半事实可解释性。

即便是从仅有的条件中获得的：以反事实为指导的最佳半事实性解释能否找到？

Even-Ifs From If-Onlys: Are the Best Semi-Factual Explanations Found  Using Counterfactuals As Guides?

We introduce a novel task, called Generalized Relation Discovery (GRD), for
open-world relation extraction. GRD aims to identify unlabeled instances in
existing pre-defined relations or discover novel relations by assigning
instances to clusters as well as providing specific meanings for these
clusters. The key challenges of GRD are how to mitigate the serious model
biases caused by labeled pre-defined relations to learn effective relational
representations and how to determine the specific semantics of novel relations
during classifying or clustering unlabeled instances. We then propose a novel
framework, SFGRD, for this task to solve the above issues by learning from
semi-factuals in two stages. The first stage is semi-factual generation
implemented by a tri-view debiased relation representation module, in which we
take each original sentence as the main view and design two debiased views to
generate semi-factual examples for this sentence. The second stage is
semi-factual thinking executed by a dual-space tri-view collaborative relation
learning module, where we design a cluster-semantic space and a class-index
space to learn relational semantics and relation label indices, respectively.
In addition, we devise alignment and selection strategies to integrate two
spaces and establish a self-supervised learning loop for unlabeled data by
doing semi-factual thinking across three views. Extensive experimental results
show that SFGRD surpasses state-of-the-art models in terms of accuracy by
2.36\% $\sim$5.78\% and cosine similarity by 32.19\%$\sim$ 84.45\% for relation
label index and relation semantic quality, respectively. To the best of our
knowledge, we are the first to exploit the efficacy of semi-factuals in
relation extraction.

通过学习半事实，我们引入了一种新任务，称为泛化关系发现（GRD），用于开放世界关系提取，我们提出了 SFGRD 框架以解决此任务，并在关系标签指数和关系语义质量方面超过了现有技术模型。

学习半事实：一种无偏差和语义感知的普适关系发现框架

Learning from Semi-Factuals: A Debiased and Semantic-Aware Framework for  Generalized Relation Discovery

Recently, eXplainable AI (XAI) research has focused on counterfactual
explanations as post-hoc justifications for AI-system decisions (e.g. a
customer refused a loan might be told: If you asked for a loan with a shorter
term, it would have been approved). Counterfactuals explain what changes to the
input-features of an AI system change the output-decision. However, there is a
sub-type of counterfactual, semi-factuals, that have received less attention in
AI (though the Cognitive Sciences have studied them extensively). This paper
surveys these literatures to summarise historical and recent breakthroughs in
this area. It defines key desiderata for semi-factual XAI and reports benchmark
tests of historical algorithms (along with a novel, naieve method) to provide a
solid basis for future algorithmic developments.

该论文调查了历史和最近在可解释人工智能 (XAI) 和半事实 (semi-factuals) 领域的突破，定义了半事实 XAI 的主要要求，并报告了历史算法的基准测试（以及一种新颖的，朴素的方法），为未来的算法发展提供了坚实的基础。