39 citations · 95 across the 35 of their papers we have counts for
39 papers
Is invariance all you need for algorithmic fairness? Removing demographic information can create new bias
Aditya Parikh, Eike Petersen, Stella Frank +3
Encoded demographic information in internal model representations is a commonly assumed risk factor for algorithmic bias, with demographic representation invariance often being tou…
Investigating Label Bias and Representational Sources of Age-Related Disparities in Medical Segmentation
Aditya Parikh, Sneha Das, Aasa Feragen
Algorithmic bias in medical imaging can perpetuate health disparities, yet its causes remain poorly understood in segmentation tasks. While fairness has been extensively studied in…
Who Does Your Algorithm Fail? Investigating Age and Ethnic Bias in the MAMA-MIA Dataset
Aditya Parikh, Sneha Das, Aasa Feragen
Deep learning models aim to improve diagnostic workflows, but fairness evaluation remains underexplored beyond classification, e.g., in image segmentation. Unaddressed segmentation…
Onto-Epistemological Analysis of AI Explanations
Martina Mattioli, Eike Petersen, Aasa Feragen +2
Artificial intelligence (AI) is being applied in almost every field. At the same time, the currently dominant deep learning methods are fundamentally black-box systems that lack ex…
Bayesian generative models can flag performance loss, bias, and out-of-distribution image content
Miguel López-Pérez, Marco Miani, Valery Naranjo +2
Generative models are popular for medical imaging tasks such as anomaly detection, feature extraction, data visualization, or image generation. Since they are parameterized by deep…
Are generative models fair? A study of racial bias in dermatological image generation
Miguel López-Pérez, Søren Hauberg, Aasa Feragen
Racial bias in medicine, such as in dermatology, presents significant ethical and clinical challenges. This is likely to happen because there is a significant underrepresentation o…