9 papers
Analyzing German Parliamentary Speeches: A Machine Learning Approach for Topic and Sentiment Classification
Lukas Pätz, Moritz Beyer, Jannik Späth +4
This study investigates political discourse in the German parliament, the Bundestag, by analyzing approximately 28,000 parliamentary speeches from the last five years. Two machine…
Overcoming Algorithm Aversion with Transparency: Can Transparent Predictions Change User Behavior?
Lasse Bohlen, Sven Kruschel, Julian Rosenberger +2
Previous work has shown that allowing users to adjust a machine learning (ML) model's predictions can reduce aversion to imperfect algorithmic decisions. However, these results wer…
Unveiling Location-Specific Price Drivers: A Two-Stage Cluster Analysis for Interpretable House Price Predictions
Paul Gümmer, Julian Rosenberger, Mathias Kraus +2
House price valuation remains challenging due to localized market variations. Existing approaches often rely on black-box machine learning models, which lack interpretability, or s…
Exploring Agentic Artificial Intelligence Systems: Towards a Typological Framework
Christopher Wissuchek, Patrick Zschech
Artificial intelligence (AI) systems are evolving beyond passive tools into autonomous agents capable of reasoning, adapting, and acting with minimal human intervention. Despite th…
Navigating the Rashomon Effect: How Personalization Can Help Adjust Interpretable Machine Learning Models to Individual Users
Julian Rosenberger, Philipp Schröppel, Sven Kruschel +3
The Rashomon effect describes the observation that in machine learning (ML) multiple models often achieve similar predictive performance while explaining the underlying relationshi…
Beware of "Explanations" of AI
David Martens, Galit Shmueli, Theodoros Evgeniou +14
Understanding the decisions made and actions taken by increasingly complex AI system remains a key challenge. This has led to an expanding field of research in explainable artifici…