Advancing Pattern Detection, Theory Development and Decision Making with Explainable AI

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.

Level thesis:degree_level

thesis.doctoral

Grantor dc:publisher

Universität Passau

Year

2025

Author dc:creator

• Stoffels, Dominik

Contributors dc:contributor

• Fiedler, Marina
• Otto, Alena

• Explainable Artificial Intelligence
• Machine Learning
• Computationally Intensive Theory Development

Statement dc:rights

• Standardbedingung laut Einverständniserklärung

Repository record source_url

https://opus4.kobv.de/opus4-uni-passau/frontdoor/index/index/docId/1597

OAI identifier oai:identifier

oai:kobv.de-opus4-uni-passau:1597