Search Results for author:
Found 6 papers, 2 papers with code
“It’s our fault!”: Insights Into Users’ Understanding and Interaction With an Explanatory Collaborative Dialog System
We conclude that creating shared mental models between users and AI systems is important to achieving successful dialogs.
Do We Need Explainable AI in Companies? Investigation of Challenges, Expectations, and Chances from Employees' Perspective
By providing insights into employees' needs and attitudes towards (X)AI, our project report contributes to the development of XAI solutions that meet the requirements of companies and their employees, ultimately driving the successful adoption of AI technologies in the business context.
What Do End-Users Really Want? Investigation of Human-Centered XAI for Mobile Health Apps
In healthcare, AI systems support clinicians and patients in diagnosis, treatment, and monitoring, but many systems' poor explainability remains challenging for practical application.
Alterfactual Explanations -- The Relevance of Irrelevance for Explaining AI Systems
We evaluate our approach in an extensive user study, revealing that it is able to significantly contribute to the participants' understanding of an AI.
GANterfactual - Counterfactual Explanations for Medical Non-Experts using Generative Adversarial Learning
By doing so, the users of counterfactual explanation systems are equipped with a completely different kind of explanatory information.
Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps
Specifically, we augment strategy summaries that extract important trajectories of states from simulations of the agent with saliency maps which show what information the agent attends to.
