174 citations · 377 across the 16 of their papers we have counts for
20 papers
Collaborative Synthetic Data for Privacy-Preserving Financial Fraud Detection Across Organizational Silos
Simeon Allmendinger, Domenique Zipperling, Burhanettin Bahadir Kibar +2
Organizations seek analytical value from AI, yet relevant data are often fragmented across organizations and constrained by privacy. This is acute in financial fraud detection, whe…
Enabling and Understanding Personalization in AI-Generated Advertising Imagery
Victor Kolominsky-Rabas, Leopold Müller, Claudius Budcke +2
Personalized marketing traditionally matches static products to customers, while dynamic creative optimization focuses mainly on AI-driven text personalization or basic product ima…
I Am AdMan: A Pipeline for Automatic Generation of Personalized Advertising Imagery
Victor Kolominsky-Rabas, Leopold Müller, Claudius Budcke +1
Personalized marketing can increase customer engagement, satisfaction, and conversion. While existing personalization approaches have become effective at matching the right product…
Generative AI Use Cases In Real Estate Marketing: Adoption and Constraints in Germany
Victor Kolominsky-Rabas, Leopold Müller, Felicia Perpina +1
Generative artificial intelligence (GenAI) is changing how work is organized and performed. Real estate marketing is a prime example of this, yet evidence of GenAI in real estate a…
It's only fair when I think it's fair: How Gender Bias Alignment Undermines Distributive Fairness in Human-AI Collaboration
Domenique Zipperling, Luca Deck, Julia Lanzl +1
Human-AI collaboration is increasingly relevant in consequential areas where AI recommendations support human discretion. However, human-AI teams' effectiveness, capability, and fa…
The Bidirectional Relationship Between XAI and Regulation: Operationalizing XAI for the AI Act
Anton Hummel, Håkan Burden, Susanne Stenberg +2
The EU AI Act makes explainability urgent for high-risk AI systems, yet most XAI research focuses on technical metrics rather than regulatory compliance. Understanding how legal re…
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…
Don't be Fooled: The Misinformation Effect of Explanations in Human-AI Collaboration
Philipp Spitzer, Joshua Holstein, Katelyn Morrison +3
Across various applications, humans increasingly use black-box artificial intelligence (AI) systems without insight into these systems' reasoning. To counter this opacity, explaina…
A Survey of AI Reliance
Sven Eckhardt, Niklas Kühl, Mateusz Dolata +1
Although artificial intelligence (AI) systems are becoming increasingly indispensable, research into how humans rely on these systems (AI reliance) is lagging behind. To advance th…
CollaFuse: Collaborative Diffusion Models
Simeon Allmendinger, Domenique Zipperling, Lukas Struppek +1
In the landscape of generative artificial intelligence, diffusion-based models have emerged as a promising method for generating synthetic images. However, the application of diffu…
Mapping the Potential of Explainable AI for Fairness Along the AI Lifecycle
Luca Deck, Astrid Schomäcker, Timo Speith +3
The widespread use of artificial intelligence (AI) systems across various domains is increasingly surfacing issues related to algorithmic fairness, especially in high-stakes scenar…
A Critical Survey on Fairness Benefits of Explainable AI
Luca Deck, Jakob Schoeffer, Maria De-Arteaga +1
In this critical survey, we analyze typical claims on the relationship between explainable AI (XAI) and fairness to disentangle the multidimensional relationship between these two…
ML-Based Teaching Systems: A Conceptual Framework
Philipp Spitzer, Niklas Kühl, Daniel Heinz +1
As the shortage of skilled workers continues to be a pressing issue, exacerbated by demographic change, it is becoming a critical challenge for organizations to preserve the knowle…
Enabling Inter-organizational Analytics in Business Networks Through Meta Machine Learning
Robin Hirt, Niklas Kühl, Dominik Martin +1
Successful analytics solutions that provide valuable insights often hinge on the connection of various data sources. While it is often feasible to generate larger data pools within…
Artificial Intelligence for Sustainability: Facilitating Sustainable Smart Product-Service Systems with Computer Vision
Jannis Walk, Niklas Kühl, Michael Saidani +1
The usage and impact of deep learning for cleaner production and sustainability purposes remain little explored. This work shows how deep learning can be harnessed to increase sust…
Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanations
Max Schemmer, Niklas Kühl, Carina Benz +2
AI advice is becoming increasingly popular, e.g., in investment and medical treatment decisions. As this advice is typically imperfect, decision-makers have to exert discretion as…
Towards Avoiding the Data Mess: Industry Insights from Data Mesh Implementations
Jan Bode, Niklas Kühl, Dominik Kreuzberger +2
With the increasing importance of data and artificial intelligence, organizations strive to become more data-driven. However, current data architectures are not necessarily designe…
Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Making
Jakob Schoeffer, Maria De-Arteaga, Niklas Kuehl
In this work, we study the effects of feature-based explanations on distributive fairness of AI-assisted decisions, specifically focusing on the task of predicting occupations from…
Fake or Credible? Towards Designing Services to Support Users' Credibility Assessment of News Content
Enrico Bunde, Niklas Kühl, Christian Meske
Fake news has become omnipresent in digitalized areas such as social media platforms. While being disseminated online, it also poses a threat to individuals and societies offline,…
Handling Concept Drift for Predictions in Business Process Mining
Lucas Baier, Josua Reimold, Niklas Kühl
Predictive services nowadays play an important role across all business sectors. However, deployed machine learning models are challenged by changing data streams over time which i…