Explaining $\mathcal{ELH}$ Concept Descriptions through Counterfactual Reasoning

• URL: http://arxiv.org/abs/2301.05109v2
• Date: Wed, 4 Oct 2023 14:33:47 GMT
• Title: Explaining $\mathcal{ELH}$ Concept Descriptions through Counterfactual Reasoning
• Authors: Leonie Nora Sieger, Stefan Heindorf, Yasir Mahmood, Lukas Bl\"ubaum, Axel-Cyrille Ngonga Ngomo
• Abstract summary: An intrinsically transparent way to do classification is by using concepts in description logics. One solution is to employ counterfactuals to answer the question, How must feature values be changed to obtain a different classification?''
• Score: 3.5323691899538128
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge bases are widely used for information management, 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 how fair the decisions are, a number of explanation approaches have been proposed. An intrinsically transparent way to do classification is by using concepts in description logics. However, these concepts can become long and difficult to fathom for non-experts, even when verbalized. One solution is to employ counterfactuals to answer the question, ``How must feature values be changed to obtain a different classification?'' By focusing on the minimal feature changes, the explanations are short, human-friendly, and provide a clear path of action regarding the change in prediction. While previous work investigated counterfactuals for tabular data, in this paper, we transfer the notion of counterfactuals to knowledge bases and the description logic $\mathcal{ELH}$. Our approach starts by generating counterfactual candidates from concepts, followed by selecting the candidates requiring the fewest feature changes as counterfactuals. When multiple counterfactuals exist, we rank them based on the likeliness of their feature combinations. We evaluate our method by conducting a user survey to determine which counterfactual candidates participants prefer for explanation.

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