Publications (24)
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…
A Light in the Dark: Deep Learning Practices for Industrial Computer Vision
Maximilian Harl, Marvin Herchenbach, Sven Kruschel +3
In recent years, large pre-trained deep neural networks (DNNs) have revolutionized the field of computer vision (CV). Although these DNNs have been shown to be very well suited for…
Survey and Systematization of 3D Object Detection Models and Methods
Moritz Drobnitzky, Jonas Friederich, Bernhard Egger +1
Strong demand for autonomous vehicles and the wide availability of 3D sensors are continuously fueling the proposal of novel methods for 3D object detection. In this paper, we prov…
A survey of image labelling for computer vision applications
Christoph Sager, Christian Janiesch, Patrick Zschech
Supervised machine learning methods for image analysis require large amounts of labelled training data to solve computer vision problems. The recent rise of deep learning algorithm…
labelCloud: A Lightweight Domain-Independent Labeling Tool for 3D Object Detection in Point Clouds
Christoph Sager, Patrick Zschech, Niklas Kühl
Within the past decade, the rise of applications based on artificial intelligence (AI) in general and machine learning (ML) in specific has led to many significant contributions wi…
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…
Machine learning and deep learning
Christian Janiesch, Patrick Zschech, Kai Heinrich
Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-…
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…
GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints
Patrick Zschech, Sven Weinzierl, Nico Hambauer +2
The number of information systems (IS) studies dealing with explainable artificial intelligence (XAI) is currently exploding as the field demands more transparency about the intern…
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…
Hate Speech and Sentiment of YouTube Video Comments From Public and Private Sources Covering the Israel-Palestine Conflict
Simon Hofmann, Christoph Sommermann, Mathias Kraus +2
This study explores the prevalence of hate speech (HS) and sentiment in YouTube video comments concerning the Israel-Palestine conflict by analyzing content from both public and pr…
A Picture is Worth a Collaboration: Accumulating Design Knowledge for Computer-Vision-based Hybrid Intelligence Systems
Patrick Zschech, Jannis Walk, Kai Heinrich +2
Computer vision (CV) techniques try to mimic human capabilities of visual perception to support labor-intensive and time-consuming tasks like the recognition and localization of cr…
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…
The Impact of Transparency in AI Systems on Users' Data-Sharing Intentions: A Scenario-Based Experiment
Julian Rosenberger, Sophie Kuhlemann, Verena Tiefenbeck +2
Artificial Intelligence (AI) systems are frequently employed in online services to provide personalized experiences to users based on large collections of data. However, AI systems…
Where Was COVID-19 First Discovered? Designing a Question-Answering System for Pandemic Situations
Johannes Graf, Gino Lancho, Patrick Zschech +1
The COVID-19 pandemic is accompanied by a massive "infodemic" that makes it hard to identify concise and credible information for COVID-19-related questions, like incubation time,…
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.…
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…
Generative AI
Stefan Feuerriegel, Jochen Hartmann, Christian Janiesch +1
The term "generative AI" refers to computational techniques that are capable of generating seemingly new, meaningful content such as text, images, or audio from training data. The…
A Survey of Text Representation Methods and Their Genealogy
Philipp Siebers, Christian Janiesch, Patrick Zschech
In recent years, with the advent of highly scalable artificial-neural-network-based text representation methods the field of natural language processing has seen unprecedented grow…
IGANN Sparse: Bridging Sparsity and Interpretability with Non-linear Insight
Theodor Stoecker, Nico Hambauer, Patrick Zschech +1
Feature selection is a critical component in predictive analytics that significantly affects the prediction accuracy and interpretability of models. Intrinsic methods for feature s…
Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning Models
Sven Kruschel, Nico Hambauer, Sven Weinzierl +3
Machine learning is permeating every conceivable domain to promote data-driven decision support. The focus is often on advanced black-box models due to their assumed performance ad…
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…
Quantifying Visual Properties of GAM Shape Plots: Impact on Perceived Cognitive Load and Interpretability
Sven Kruschel, Lasse Bohlen, Julian Rosenberger +2
Generalized Additive Models (GAMs) offer a balance between performance and interpretability in machine learning. The interpretability aspect of GAMs is expressed through shape plot…
A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis
Sandra Zilker, Sven Weinzierl, Mathias Kraus +2
Proactive analysis of patient pathways helps healthcare providers anticipate treatment-related risks, identify outcomes, and allocate resources. Machine learning (ML) can leverage…