Explainable Artificial Intelligence and Formal Argumentation have received
significant attention in recent years. Argumentation-based systems often lack
explainability while supporting decision-making processes. Counterfactual and
semifactual explanations are interpretability techniques that provide insights
into the outcome of a model by generating alternative hypothetical instances.
While there has been important work on counterfactual and semifactual
explanations for Machine Learning models, less attention has been devoted to
these kinds of problems in argumentation. In this paper, we explore
counterfactual and semifactual reasoning in abstract Argumentation Framework.
We investigate the computational complexity of counterfactual- and
semifactual-based reasoning problems, showing that they are generally harder
than classical argumentation problems such as credulous and skeptical
acceptance. Finally, we show that counterfactual and semifactual queries can be
encoded in weak-constrained Argumentation Framework, and provide a
computational strategy through ASP solvers.

该论文探讨了可解释人工智能和形式论证在抽象论证框架中的反事实和半事实推理，研究了其计算复杂性，并提供了通过 ASP 求解器的计算策略。

抽象论证中的对立假设和半事实解释：形式基础、复杂性与计算

Counterfactual and Semifactual Explanations in Abstract Argumentation:  Formal Foundations, Complexity and Computation

Machine learning based decision making systems applied in safety critical
areas require reliable high certainty predictions. For this purpose, the system
can be extended by an reject option which allows the system to reject inputs
where only a prediction with an unacceptably low certainty would be possible.
While being able to reject uncertain samples is important, it is also of
importance to be able to explain why a particular sample was rejected. With the
ongoing rise of eXplainable AI (XAI), a lot of explanation methodologies for
machine learning based systems have been developed -- explaining reject
options, however, is still a novel field where only very little prior work
exists.
In this work, we propose to explain rejects by semifactual explanations, an
instance of example-based explanation methods, which them self have not been
widely considered in the XAI community yet. We propose a conceptual modeling of
semifactual explanations for arbitrary reject options and empirically evaluate
a specific implementation on a conformal prediction based reject option.

本文提出利用半事实解释拒绝选项的概念模型，以实现机器学习中的可解释性，并通过实验对拒绝选项进行了评估。