most citedWhat Makes for a Good Saliency Map? Comparing Strategies for Evaluating Saliency Maps in Explainable AI (XAI)

3 citations · 3 across the 2 of their papers we have counts for

collaborators

5 papers

cs.LG2026

From Universal to Individualized Actionability: Revisiting Personalization in Algorithmic Recourse

Lena Marie Budde, Ayan Majumdar, Richard Uth +2

Algorithmic recourse aims to provide actionable recommendations that enable individuals to change unfavorable model outcomes, and prior work has extensively studied properties such…

cs.HC2026

Don't blame me: How Intelligent Support Affects Moral Responsibility in Human Oversight

Cedric Faas, Richard Uth, Sarah Sterz +2

AI-based systems can increasingly perform work tasks autonomously. In safety-critical tasks, human oversight of these systems is required to mitigate risks and to ensure responsibi…

cs.HC2025

Design Considerations for Human Oversight of AI: Insights from Co-Design Workshops and Work Design Theory

Cedric Faas, Sophie Kerstan, Richard Uth +2

As AI systems become increasingly capable and autonomous, domain experts' roles are shifting from performing tasks themselves to overseeing AI-generated outputs. Such oversight is…

cs.HC20253 cited

What Makes for a Good Saliency Map? Comparing Strategies for Evaluating Saliency Maps in Explainable AI (XAI)

Felix Kares, Timo Speith, Hanwei Zhang +1

Saliency maps are a popular approach for explaining classifications of (convolutional) neural networks. However, it remains an open question as to how best to evaluate salience map…

cs.HC2025

On the Complexities of Testing for Compliance with Human Oversight Requirements in AI Regulation

Markus Langer, Veronika Lazar, Kevin Baum

Human oversight requirements are a core component of the European AI Act and in AI governance. In this paper, we highlight key challenges in testing for compliance with these requi…