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Found 8 papers, 3 papers with code
Slicing Through Bias: Explaining Performance Gaps in Medical Image Analysis using Slice Discovery Methods
Sex-based differences in the prevalence of these shortcut features appear to cause the observed classification performance gap, representing a previously underappreciated interaction between shortcut learning and model fairness analyses.
Diffusion-based Iterative Counterfactual Explanations for Fetal Ultrasound Image Quality Assessment
Obstetric ultrasound image quality is crucial for accurate diagnosis and monitoring of fetal health.
Generalizing Fairness to Generative Language Models via Reformulation of Non-discrimination Criteria
Our results address the presence of occupational gender bias within such conversational language models.
Shortcut Learning in Medical Image Segmentation
Shortcut learning is a phenomenon where machine learning models prioritize learning simple, potentially misleading cues from data that do not generalize well beyond the training set.
Fast Diffusion-Based Counterfactuals for Shortcut Removal and Generation
Via a novel inpainting-based modification we spatially limit the changes made with no extra inference step, encouraging the removal of spatially constrained shortcut features while ensuring that the shortcut-free counterfactuals preserve their remaining image features to a high degree.
Are Sex-based Physiological Differences the Cause of Gender Bias for Chest X-ray Diagnosis?
In this work, we investigate the causes of gender bias in machine learning-based chest X-ray diagnosis.
An Interpretable and Attention-based Method for Gaze Estimation Using Electroencephalography
Eye movements can reveal valuable insights into various aspects of human mental processes, physical well-being, and actions.
Will You Ever Become Popular? Learning to Predict Virality of Dance Clips
Therefore, virality prediction from dance challenges is of great commercial value and has a wide range of applications, such as smart recommendation and popularity promotion.
