174 citations · 425 across the 33 of their papers we have counts for
34 papers
Think Once, Reflect Twice: How Feedback Shapes Human-AI Collaboration
Joshua Holstein, Philipp Spitzer, Sebastian Schäfer +2
As artificial intelligence (AI) increasingly supports human decision-making in high-stakes domains, designing effective human-AI collaboration becomes critical. Successful collabor…
From Model Uncertainty to Human Attention: Localization-Aware Visual Cues for Scalable Annotation Review
Moussa Kassem Sbeyti, Joshua Holstein, Philipp Spitzer +2
High-quality labeled data is essential for training robust machine learning models, yet obtaining annotations at scale remains expensive. AI-assisted annotation has therefore becom…
When Thinking Pays Off: Incentive Alignment for Human-AI Collaboration
Joshua Holstein, Patrick Hemmer, Gerhard Satzger +1
Collaboration with artificial intelligence (AI) has improved human decision-making across various domains by leveraging the complementary capabilities of humans and AI. Yet, humans…
Development of Mental Models in Human-AI Collaboration: A Conceptual Framework
Joshua Holstein, Gerhard Satzger
Artificial intelligence has become integral to organizational decision-making and while research has explored many facets of this human-AI collaboration, the focus has mainly been…
Data Quality Challenges in Retrieval-Augmented Generation
Leopold Müller, Joshua Holstein, Sarah Bause +2
Organizations increasingly adopt Retrieval-Augmented Generation (RAG) to enhance Large Language Models with enterprise-specific knowledge. However, current data quality (DQ) framew…
Honey, I Shrunk the Language Model: Impact of Knowledge Distillation Methods on Performance and Explainability
Daniel Hendriks, Philipp Spitzer, Niklas Kühl +1
Artificial Intelligence (AI) has increasingly influenced modern society, recently in particular through significant advancements in Large Language Models (LLMs). However, high comp…
Towards Human-Understandable Multi-Dimensional Concept Discovery
Arne Grobrügge, Niklas Kühl, Gerhard Satzger +1
Concept-based eXplainable AI (C-XAI) aims to overcome the limitations of traditional saliency maps by converting pixels into human-understandable concepts that are consistent acros…
Explainability in AI Based Applications: A Framework for Comparing Different Techniques
Arne Grobrugge, Nidhi Mishra, Johannes Jakubik +1
The integration of artificial intelligence into business processes has significantly enhanced decision-making capabilities across various industries such as finance, healthcare, an…
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…
Transferring Domain Knowledge with (X)AI-Based Learning Systems
Philipp Spitzer, Niklas Kühl, Marc Goutier +2
In numerous high-stakes domains, training novices via conventional learning systems does not suffice. To impart tacit knowledge, experts' hands-on guidance is imperative. However,…
Improving Label Error Detection and Elimination with Uncertainty Quantification
Johannes Jakubik, Michael Vössing, Manil Maskey +2
Identifying and handling label errors can significantly enhance the accuracy of supervised machine learning models. Recent approaches for identifying label errors demonstrate that…
Complementarity in Human-AI Collaboration: Concept, Sources, and Evidence
Patrick Hemmer, Max Schemmer, Niklas Kühl +2
Artificial intelligence (AI) has the potential to significantly enhance human performance across various domains. Ideally, collaboration between humans and AI should result in comp…
Human Delegation Behavior in Human-AI Collaboration: The Effect of Contextual Information
Philipp Spitzer, Joshua Holstein, Patrick Hemmer +4
The integration of artificial intelligence (AI) into human decision-making processes at the workplace presents both opportunities and challenges. One promising approach to leverage…
Towards Effective Human-AI Decision-Making: The Role of Human Learning in Appropriate Reliance on AI Advice
Max Schemmer, Andrea Bartos, Philipp Spitzer +4
The true potential of human-AI collaboration lies in exploiting the complementary capabilities of humans and AI to achieve a joint performance superior to that of the individual AI…
Improving the Efficiency of Human-in-the-Loop Systems: Adding Artificial to Human Experts
Johannes Jakubik, Daniel Weber, Patrick Hemmer +2
Information systems increasingly leverage artificial intelligence (AI) and machine learning (ML) to generate value from vast amounts of data. However, ML models are imperfect and c…
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…
AI Reliance and Decision Quality: Fundamentals, Interdependence, and the Effects of Interventions
Jakob Schoeffer, Johannes Jakubik, Michael Voessing +2
In AI-assisted decision-making, a central promise of having a human-in-the-loop is that they should be able to complement the AI system by overriding its wrong recommendations. In…
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…
Human-AI Collaboration: The Effect of AI Delegation on Human Task Performance and Task Satisfaction
Patrick Hemmer, Monika Westphal, Max Schemmer +3
Recent work has proposed artificial intelligence (AI) models that can learn to decide whether to make a prediction for an instance of a task or to delegate it to a human by conside…
Towards Meaningful Anomaly Detection: The Effect of Counterfactual Explanations on the Investigation of Anomalies in Multivariate Time Series
Max Schemmer, Joshua Holstein, Niklas Bauer +2
Detecting rare events is essential in various fields, e.g., in cyber security or maintenance. Often, human experts are supported by anomaly detection systems as continuously monito…
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…
Data-Centric Artificial Intelligence
Johannes Jakubik, Michael Vössing, Niklas Kühl +2
Data-centric artificial intelligence (data-centric AI) represents an emerging paradigm emphasizing that the systematic design and engineering of data is essential for building effe…
Forming Effective Human-AI Teams: Building Machine Learning Models that Complement the Capabilities of Multiple Experts
Patrick Hemmer, Sebastian Schellhammer, Michael Vössing +2
Machine learning (ML) models are increasingly being used in application domains that often involve working together with human experts. In this context, it can be advantageous to d…
On the Effect of Information Asymmetry in Human-AI Teams
Patrick Hemmer, Max Schemmer, Niklas Kühl +2
Over the last years, the rising capabilities of artificial intelligence (AI) have improved human decision-making in many application areas. Teaming between AI and humans may even l…