2026-09-21T14:35:28Z https://opus4.kobv.de/opus4-uni-passau/oai

oai:kobv.de-opus4-uni-passau:1597 2025-08-13 bibliography:false doc-type:PhDThesis open_access ddc ddc:004 ddc:330

Advancing Pattern Detection, Theory Development and Decision Making with Explainable AI Dominik Stoffels 004 330 Explainable Artificial Intelligence Machine Learning Computationally Intensive Theory Development The application of explainable artificial intelligence (XAI) methods in data-driven decision-making and computationally intensive theory development (CTD) is a subject of ongoing debate, particularly concerning how and whether these methods can be effectively employed, and how the reliability of their explanations can be ensured. This dissertation addresses these issues by systematically analyzing the usability of XAI for pattern detection, CTD, and decision-making, drawing on various real-world and synthetic datasets and employing different empirical methods and perspectives. The dissertation consists of four studies, each addressing distinct issues in the field of XAI application. Universität Passau Passau Innstrasse 29, 94032 Passau Marina Fiedler Alena Otto 2025-05-07 2025-06-06 PhDThesis Text urn:nbn:de:bvb:739-opus4-15975 application/pdf eng Standardbedingung laut Einverständniserklärung thesis.doctoral Universität Passau Passau Wirtschaftswissenschaftliche Fakultät 1 https://opus4.kobv.de/opus4-uni-passau/oai/container/index/docId/1597 https://opus4.kobv.de/opus4-uni-passau/frontdoor/index/index/docId/1597