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Simone Kopeinik

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Articles

Computer Science > Information Retrieval

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By Dominik Kowald, Simone Kopeinik, Gregor Autischer
| arXiv
AI-based labor-market systems or platforms can affect access to job opportunities prior to organizational candidate rankings or hiring decisions. Such applications warrant caution, as biases in skill extraction, profile formation, and candidate-job matching may contribute to unfair treatment of candidates. In this paper, we propose a two-stage framework for detecting and governing bias in skills-based job matching.
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Multistakeholder Fairness in Tourism: What can Algorithms learn from Tourism Management?

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By Anna Schreuer, Simone Kopeinik, Bernhard U. Wieser, Dominik Kowald
| arXiv
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Exploring Gender Bias in Large Language Models: An In-depth Dive into the German Language

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By Simone Kopeinik, Ralf Schlüter, David Thulke
| arXiv
arXiv:2507.16557 (cs) View PDF Comments: Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG) Cite as: arXiv:2507.16557 [cs.CL] (or arXiv:2507.16557v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2507.16557 Submission history From: David Thulke [ view email] [v1] Tue, 22 Jul 2025 13:09:41 UTC (997 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle...
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