24 citations · 57 across the 7 of their papers we have counts for
8 papers
Large Language Models Do Not Simulate Human Psychology
Sarah Schröder, Thekla Morgenroth, Ulrike Kuhl +2
Large Language Models (LLMs),such as ChatGPT, are increasingly used in research, ranging from simple writing assistance to complex data annotation tasks. Recently, some research ha…
When Bias Backfires: The Modulatory Role of Counterfactual Explanations on the Adoption of Algorithmic Bias in XAI-Supported Human Decision-Making
Ulrike Kuhl, Annika Bush
Although the integration of artificial intelligence (AI) into everyday tasks improves efficiency and objectivity, it also risks transmitting bias to human decision-making. In this…
Interpretable Event Diagnosis in Water Distribution Networks
André Artelt, Stelios G. Vrachimis, Demetrios G. Eliades +3
The increasing penetration of information and communication technologies in the design, monitoring, and control of water systems enables the use of algorithms for detecting and ide…
Toward enriched Cognitive Learning with XAI
Muhammad Suffian, Ulrike Kuhl, Jose M. Alonso-Moral +1
As computational systems supported by artificial intelligence (AI) techniques continue to play an increasingly pivotal role in making high-stakes recommendations and decisions acro…
For Better or Worse: The Impact of Counterfactual Explanations' Directionality on User Behavior in xAI
Ulrike Kuhl, André Artelt, Barbara Hammer
Counterfactual explanations (CFEs) are a popular approach in explainable artificial intelligence (xAI), highlighting changes to input data necessary for altering a model's output.…
Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract Setting
Ulrike Kuhl, André Artelt, Barbara Hammer
Counterfactual explanations (CFEs) highlight what changes to a model's input would have changed its prediction in a particular way. CFEs have gained considerable traction as a psyc…
Let's Go to the Alien Zoo: Introducing an Experimental Framework to Study Usability of Counterfactual Explanations for Machine Learning
Ulrike Kuhl, André Artelt, Barbara Hammer
To foster usefulness and accountability of machine learning (ML), it is essential to explain a model's decisions in addition to evaluating its performance. Accordingly, the field o…
Intuitiveness in Active Teaching
Jan Philip Göpfert, Ulrike Kuhl, Lukas Hindemith +2
While Machine learning gives rise to astonishing results in automated systems, it is usually at the cost of large data requirements. This makes many successful algorithms from mach…