49 citations · 59 across the 6 of their papers we have counts for
6 papers
Detecting Systematic Weaknesses in Vision Models along Predefined Human-Understandable Dimensions
Sujan Sai Gannamaneni, Rohil Prakash Rao, Michael Mock +2
Slice discovery methods (SDMs) are prominent algorithms for finding systematic weaknesses in DNNs. They identify top-k semantically coherent slices/subsets of data where a DNN-unde…
Assessing Systematic Weaknesses of DNNs using Counterfactuals
Sujan Sai Gannamaneni, Michael Mock, Maram Akila
With the advancement of DNNs into safety-critical applications, testing approaches for such models have gained more attention. A current direction is the search for and identificat…
Using ScrutinAI for Visual Inspection of DNN Performance in a Medical Use Case
Rebekka Görge, Elena Haedecke, Michael Mock
Our Visual Analytics (VA) tool ScrutinAI supports human analysts to investigate interactively model performanceand data sets. Model performance depends on labeling quality to a lar…
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…
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…