110 citations · 187 across the 10 of their papers we have counts for
10 papers
From Attribution to Action: A Human-Centered Application of Activation Steering
Tobias Labarta, Maximilian Dreyer, Katharina Weitz +2
Explainable AI (XAI) methods reveal which features influence model predictions, yet provide limited means for practitioners to act on these explanations. Activation steering of com…
Communicating Through Avatars in Industry 5.0: A Focus Group Study on Human-Robot Collaboration
Stina Klein, Pooja Prajod, Katharina Weitz +3
The integration of collaborative robots (cobots) in industrial settings raises concerns about worker well-being, particularly due to reduced social interactions. Avatars - designed…
See What I Mean? CUE: A Cognitive Model of Understanding Explanations
Tobias Labarta, Nhi Hoang, Katharina Weitz +3
As machine learning systems increasingly inform critical decisions, the need for human-understandable explanations grows. Current evaluations of Explainable AI (XAI) often prioriti…
Relevant Irrelevance: Generating Alterfactual Explanations for Image Classifiers
Silvan Mertes, Tobias Huber, Christina Karle +4
In this paper, we demonstrate the feasibility of alterfactual explanations for black box image classifiers. Traditional explanation mechanisms from the field of Counterfactual Thin…
Giving Robots a Voice: Human-in-the-Loop Voice Creation and open-ended Labeling
Pol van Rijn, Silvan Mertes, Kathrin Janowski +3
Speech is a natural interface for humans to interact with robots. Yet, aligning a robot's voice to its appearance is challenging due to the rich vocabulary of both modalities. Prev…
What Do End-Users Really Want? Investigation of Human-Centered XAI for Mobile Health Apps
Katharina Weitz, Alexander Zellner, Elisabeth André
In healthcare, AI systems support clinicians and patients in diagnosis, treatment, and monitoring, but many systems' poor explainability remains challenging for practical applicati…
Do We Need Explainable AI in Companies? Investigation of Challenges, Expectations, and Chances from Employees' Perspective
Katharina Weitz, Chi Tai Dang, Elisabeth André
Companies' adoption of artificial intelligence (AI) is increasingly becoming an essential element of business success. However, using AI poses new requirements for companies and th…
Alterfactual Explanations -- The Relevance of Irrelevance for Explaining AI Systems
Silvan Mertes, Christina Karle, Tobias Huber +3
Explanation mechanisms from the field of Counterfactual Thinking are a widely-used paradigm for Explainable Artificial Intelligence (XAI), as they follow a natural way of reasoning…
GANterfactual - Counterfactual Explanations for Medical Non-Experts using Generative Adversarial Learning
Silvan Mertes, Tobias Huber, Katharina Weitz +2
With the ongoing rise of machine learning, the need for methods for explaining decisions made by artificial intelligence systems is becoming a more and more important topic. Especi…
Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps
Tobias Huber, Katharina Weitz, Elisabeth André +1
With advances in reinforcement learning (RL), agents are now being developed in high-stakes application domains such as healthcare and transportation. Explaining the behavior of th…