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Beyond Binary Boundaries—Exploring the Potential of Categorical Variables in Counterfactual Explanations

Author

Listed:
  • Philipp Hühn

    (Ulm University, Institute of Business Analytics)

  • Simon Hofer

    (Ulm University, Institute of Business Analytics)

Abstract

The rapid development of Artificial Intelligence (AI) has brought about significant changes in various industries. However, the opaqueness of AI systems leads to blind trust, distrust, or even avoidance of the systems altogether. Against this background, counterfactual explanation methods generate human-understandable explanations of how an AI system arrives at a given output. However, the thorough integration of categorical variables in the generation of counterfactual explanations remains a challenging research problem. In this paper, we investigate by means of a user study how a human-like handling of categorical variables affects users’ perception of explanations for a state-of-the-art counterfactual explanation approach. The results show that using a human-like handling of categorical variables leads to the generation of explanations preferred by users. Moreover, we find that the handling of categorical variables affects users’ perceptions of the concreteness, coherence, and relevance of the generated counterfactual explanations.

Suggested Citation

  • Philipp Hühn & Simon Hofer, 2025. "Beyond Binary Boundaries—Exploring the Potential of Categorical Variables in Counterfactual Explanations," Lecture Notes in Information Systems and Organization,, Springer.
    • Handle: RePEc:spr:lnichp:978-3-031-80122-8_11
    DOI: 10.1007/978-3-031-80122-8_11
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