24 citations · 57 across the 7 of their papers we have counts for
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cs.HC2025★ 4 cited
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…
cs.HC2022★ 22 cited
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…
cs.HC2020
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…