A new study published on arXiv highlights significant skin-tone bias in dermatology concept datasets, specifically the widely used SkinCon dataset. The research found that erythema, a key indicator of skin inflammation, is annotated less frequently on darker skin tones compared to lighter ones, with an odds ratio of 0.17. Conversely, hyperpigmentation and hypopigmentation are annotated more often on darker skin. This tone-dependent annotation pattern is learned by concept bottleneck models, potentially leading to diagnostic errors where the model agrees with omissions rather than correcting them. The study suggests that concept annotations must be audited for subgroup biases before relying on AI models trained with them for clinical explanations. AI
IMPACT Highlights the critical need for bias auditing in AI training data to ensure equitable performance across diverse populations.
RANK_REASON Research paper published on arXiv detailing bias in a dermatology dataset. [lever_c_demoted from research: ic=1 ai=1.0]
- Concept bottleneck models
- erythema
- hyperpigmentation
- hypopigmentation
- Nikos Papanikolopoulos
- SkinCon
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