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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…
False Confidence: Automated Labels Confound Fairness Audits in Cervical Spine Segmentation
Linus Juni, Aasa Feragen, Aditya Parikh
Automated segmentation of cervical-spine MRI is increasingly used in clinical workflows, yet no fairness audit exists for this anatomy. We show that auditing these segmentation tas…
Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation
Aditya Parikh, Stella Frank, Sneha Das +1
Biases in annotation pipelines may introduce label bias: when annotation quality or style varies systematically across demographic subgroups, causing disparities in performance and…