2 citations · 3 across the 9 of their papers we have counts for
10 papers · 1 filter
Can Conversational XAI Improve User Performance? An Experimental Study
Sven Kruschel, Julian Rosenberger, Lasse Bohlen +2
Explainable AI (XAI) techniques aim to provide insights into predictive models and enhance user performance, yet they often fall short of these expectations. Conversational XAI ass…
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…
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…
CareerBERT: Matching Resumes to ESCO Jobs in a Shared Embedding Space for Generic Job Recommendations
Julian Rosenberger, Lukas Wolfrum, Sven Weinzierl +2
The rapidly evolving labor market, driven by technological advancements and economic shifts, presents significant challenges for traditional job matching and consultation services.…