False Confidence: Automated Labels Confound Fairness Audits in Cervical Spine Segmentation
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 tasks is complicated by a common property of modern segmentation datasets: expert-annotated gold labels are expensive, so abundant machine-generated (silver) labels are added to limit annotation cost. This matters because the reference used to judge a model can itself be biased. In this study, we present the first fairness audit of cervical-spine MRI segmentation across sex, age, and race using the CSpineSeg dataset. We observe that the deployed model is demographically fair, but the choice of reference label, howe...
| Authors | Linus Juni, Aasa Feragen, Aditya Parikh |
|---|---|
| Primary category | eess.IV |
| Published | 8 July 2026 |
| Last updated at source | 8 July 2026 |
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