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Evidence record 114 · automatically gathered

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 fairnes

Record details

Published: 8 July 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Healthcare · Transparency
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Bias & fairness · Shared topic: Healthcare

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Shared topic: Bias & fairness · Shared topic: Healthcare

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Shared topic: Bias & fairness · Shared topic: Healthcare

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How to cite this record

ethics.ai (8 July 2026), “False Confidence: Automated Labels Confound Fairness Audits in Cervical Spine Segmentation,” evidence record 114, https://ethics.ai/record/114 (originally published by arXiv).

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