49 citations · 59 across the 5 of their papers we have counts for
5 papers
Guideline for Trustworthy Artificial Intelligence -- AI Assessment Catalog
Maximilian Poretschkin, Anna Schmitz, Maram Akila +11
Artificial Intelligence (AI) has made impressive progress in recent years and represents a key technology that has a crucial impact on the economy and society. However, it is clear…
Validation of Simulation-Based Testing: Bypassing Domain Shift with Label-to-Image Synthesis
Julia Rosenzweig, Eduardo Brito, Hans-Ulrich Kobialka +8
Many machine learning applications can benefit from simulated data for systematic validation - in particular if real-life data is difficult to obtain or annotate. However, since si…
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety
Sebastian Houben, Stephanie Abrecht, Maram Akila +38
The use of deep neural networks (DNNs) in safety-critical applications like mobile health and autonomous driving is challenging due to numerous model-inherent shortcomings. These s…
Patch Shortcuts: Interpretable Proxy Models Efficiently Find Black-Box Vulnerabilities
Julia Rosenzweig, Joachim Sicking, Sebastian Houben +2
An important pillar for safe machine learning (ML) is the systematic mitigation of weaknesses in neural networks to afford their deployment in critical applications. An ubiquitous…
Information-Theoretic Perspective of Federated Learning
Linara Adilova, Julia Rosenzweig, Michael Kamp
An approach to distributed machine learning is to train models on local datasets and aggregate these models into a single, stronger model. A popular instance of this form of parall…