Trapp, Anna ORCID: 0009-0008-6992-7644
(2024).
Generation of Counterfactual Explanations in Smart Environments.
Masters thesis, Universität zu Köln.
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Abstract
Explainability is increasingly seen as an essential feature of rule-based smart environments. While counterfactual explanations, which describe what could have been done differently to achieve a desired outcome, are a powerful tool in eXplainable AI (XAI), no established methods exist for generating them in these rule-based domains. In this paper, we present the first formalization and implementation of counterfactual explanations tailored to this domain. It is implemented as a plugin that extends an existing explanation engine for smart environments. We conducted a human-centered study (N=17) to evaluate our generated counterfactuals against traditional causal explanations. The results show that user preference is highly contextual: causal explanations are favored for their linguistic simplicity and in time-pressured situations, while counterfactuals are preferred for their actionable content, particularly when a user wants to resolve a problem. Our work contributes a practical framework for a new type of explanation in smart environments and provides empirical evidence to guide the choice of when each explanation type is most effective.
Item Type: | Thesis (Masters thesis) | ||||||||||
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URN: | urn:nbn:de:hbz:38-788134 | ||||||||||
Date: | 23 December 2024 | ||||||||||
Language: | English | ||||||||||
Faculty: | Faculty of Mathematics and Natural Sciences | ||||||||||
Divisions: | Faculty of Mathematics and Natural Sciences > Department of Mathematics and Computer Science > Institute of Computer Science | ||||||||||
Subjects: | Data processing Computer science | ||||||||||
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Date of oral exam: | 10 February 2025 | ||||||||||
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Refereed: | Yes | ||||||||||
URI: | http://kups.ub.uni-koeln.de/id/eprint/78813 |
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