Knowledge bases are widely used for information management on the web, enabling high-impact applications such as web search, question answering, and natural language processing. They also serve as the backbone for automatic decision systems, e.g. for medical diagnostics and credit scoring. As stakeholders affected by these decisions would like to understand their situation and verify fair decisions, a number of explanation approaches have been proposed using concepts in description logics. However, the learned concepts can become long and difficult to fathom for non-experts, even when verbalized. Moreover, long concepts do not immediately provide a clear path of action to change one's situation. Counterfactuals answering the question "How must feature values be changed to obtain a different classification?" have been proposed as short, human-friendly explanations for tabular data. In this paper, we transfer the notion of counterfactuals to description logics and propose the first algorithm for generating counterfactual explanations in the description logic $\mathcal{ELH}$. Counterfactual candidates are generated from concepts and the candidates with fewest feature changes are selected as counterfactuals. In case of multiple counterfactuals, we rank them according to the likeliness of their feature combinations. For evaluation, we conduct a user survey to investigate which of the generated counterfactual candidates are preferred for explanation by participants. In a second study, we explore possible use cases for counterfactual explanations.
翻译:知识基础被广泛用于网上的信息管理,使网络搜索、问答和自然语言处理等高效应用成为网络搜索、问答和自然语言处理等高影响应用工具,它们也成为自动决策系统的主干,例如医疗诊断和信用评分。由于受这些决定影响的利益攸关方希望了解其状况并核实其公平决定,因此,利用描述逻辑的概念提出了若干解释方法。然而,即使口头上讲,对非专家来说,所学的概念也可能变得漫长和难以理解。此外,长期的概念并不立即为改变个人状况提供明确的行动路径。反事实回答“必须如何改变特性值以获得不同的分类”的问题已被提议为表格数据的简短、对人友好的解释。在本文中,我们将反事实概念的概念转换为描述逻辑的概念,并提出在描述逻辑 $\mathcal{ELH} $ 中产生反事实解释的第一个算法。 反事实候选人来自概念,而特征变化最少的候选人被选为反事实事实。在多个反事实假设中,我们将它们归类为反事实研究对象,我们用来进行反事实解释。