Abstract Skin tone assessments are critical for fairness evaluation in healthcare algorithms (e.g., pulse oximetry) but lack validation. Using prospectively collected facial images from 90 hospitalized adults at the San Francisco VA, three independent annotators rated facial regions in triplicate using Fitzpatrick (I–VI) and Monk (1–10) skin tone scales. Patients also self-identified their skin tone. Annotator confidence was recorded using 5-point Likert scales. Across 810 images in 90 patients (9 images each), within-rater agreement was high, but inter-annotator agreement was moderate to low. Annotators frequently rated patients as darker when patients self-identified as lighter, and lighter when patients self-identified as darker. In linear mixed-effects models controlling for facial region and annotator confidence, darker self-reported skin tones were associated with lighter annotator scores. These findings highlight challenges in consistent skin tone labeling and suggest that current methods for assessing representation in biosensor-based algorithm studies may be influenced by labeling bias.
Cu et al. (2025) studied this question.