# Skin Deep: Investigating Subjectivity in Skin Tone Annotations for Computer Vision Benchmark Datasets arXiv: https://arxiv.org/abs/2305.09072 ยท HTML: https://mlsanity.com/paper/2305.09072/ ## Key facts - Cited by 11 papers in the map (data through 14 Oct 2026). - Builds on 28 of its 91 references that are in the map. - First posted on arXiv 15 May 2023. ## Builds on (most cited first, 28 of 28) - [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014) - [Differential Privacy Has Disparate Impact on Model Accuracy](https://mlsanity.com/paper/1905.12101/) (2019) - [Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset](https://mlsanity.com/paper/2104.09957/) (2021) - [Disparities in Dermatology AI: Assessments Using Diverse Clinical Images](https://mlsanity.com/paper/2111.08006/) (2021) - [Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning](https://mlsanity.com/paper/1912.10389/) (2019) - [Towards Measuring Fairness in AI: the Casual Conversations Dataset](https://mlsanity.com/paper/2104.02821/) (2021) - [Diversity in Faces](https://mlsanity.com/paper/1901.10436/) (2019) - [Predictive Inequity in Object Detection](https://mlsanity.com/paper/1902.11097/) (2019) - [Whose Ground Truth? Accounting for Individual and Collective Identities Underlying Dataset Annotation](https://mlsanity.com/paper/2112.04554/) (2021) - [Bringing the People Back In: Contesting Benchmark Machine Learning Datasets](https://mlsanity.com/paper/2007.07399/) (2020) - [Towards Transparency in Dermatology Image Datasets with Skin Tone Annotations by Experts, Crowds, and an Algorithm](https://mlsanity.com/paper/2207.02942/) (2022) - [One Label, One Billion Faces: Usage and Consistency of Racial Categories in Computer Vision](https://mlsanity.com/paper/2102.02320/) (2021) - [Estimating Skin Tone and Effects on Classification Performance in Dermatology Datasets](https://mlsanity.com/paper/1910.13268/) (2019) - [Mitigating Bias in Set Selection with Noisy Protected Attributes](https://mlsanity.com/paper/2011.04219/) (2020) - [FairFace Challenge at ECCV 2020: Analyzing Bias in Face Recognition](https://mlsanity.com/paper/2009.07838/) (2020) - [Billion-Scale Pretraining with Vision Transformers for Multi-Task Visual Representations](https://mlsanity.com/paper/2108.05887/) (2021) - [Characterizing Bias in Classifiers using Generative Models](https://mlsanity.com/paper/1906.11891/) (2019) - [Reliable and Trustworthy Machine Learning for Health Using Dataset Shift Detection](https://mlsanity.com/paper/2110.14019/) (2021) - [Implicit Diversity in Image Summarization](https://mlsanity.com/paper/1901.10265/) (2019) - [Analysis of Manual and Automated Skin Tone Assignments for Face Recognition Applications](https://mlsanity.com/paper/2104.14685/) (2021) - [Measuring Hidden Bias within Face Recognition via Racial Phenotypes](https://mlsanity.com/paper/2110.09839/) (2021) - [Reliability and Validity of Image-Based and Self-Reported Skin Phenotype Metrics](https://mlsanity.com/paper/2106.11240/) (2021) - [Goldilocks: Consistent Crowdsourced Scalar Annotations with Relative Uncertainty](https://mlsanity.com/paper/2108.01799/) (2021) - [Out-of-Distribution Detection in Dermatology using Input Perturbation and Subset Scanning](https://mlsanity.com/paper/2105.11160/) (2021) - [Seeing without Looking: Analysis Pipeline for Child Sexual Abuse Datasets](https://mlsanity.com/paper/2204.14110/) (2022) - [Synthetic Data for Multi-Parameter Camera-Based Physiological Sensing](https://mlsanity.com/paper/2110.04902/) (2021) - [SREDS: A dichromatic separation based measure of skin color](https://mlsanity.com/paper/2104.02926/) (2021) - [Automatic Facial Skin Feature Detection for Everyone](https://mlsanity.com/paper/2203.16056/) (2022) ## Built on by (most cited first, 11 of 11) - [Towards Fairness in AI for Melanoma Detection: Systemic Review and Recommendations](https://mlsanity.com/paper/2411.12846/) (2024) - [Making Data Work Count](https://mlsanity.com/paper/2311.18046/) (2023) - [A Taxonomy of Challenges to Curating Fair Datasets](https://mlsanity.com/paper/2406.06407/) (2024) - [Enhancing Fairness in Skin Lesion Classification for Medical Diagnosis Using Prune Learning](https://mlsanity.com/paper/2509.00745/) (2025) - [TrueSkin: Towards Fair and Accurate Skin Tone Recognition and Generation](https://mlsanity.com/paper/2509.10980/) (2025) - [The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotation](https://mlsanity.com/paper/2602.11318/) (2026) - [Gone With the Bits: Revealing Racial Bias in Low-Rate Neural Compression for Facial Images](https://mlsanity.com/paper/2505.02949/) (2025) - [Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression](https://mlsanity.com/paper/2506.11627/) (2025) - [Neutral Prompts, Non-Neutral People: Quantifying Gender and Skin-Tone Bias in Gemini Flash 2.5 Image and GPT Image 1.5](https://mlsanity.com/paper/2602.12133/) (2026) - [Mitigating Individual Skin Tone Bias in Skin Lesion Classification through Distribution-Aware Reweighting](https://mlsanity.com/paper/2512.08733/) (2025) - [Skin Tone in the Wild: A Dataset and Benchmark for 10-tone MST Classification](https://mlsanity.com/paper/2603.02475/) (2026) ## Abstract (arXiv) To investigate the well-observed racial disparities in computer vision systems that analyze images of humans, researchers have turned to skin tone as more objective annotation than race metadata for fairness performance evaluations. However, the current state of skin tone annotation procedures is highly varied. For instance, researchers use a range of untested scales and skin tone categories, have unclear annotation procedures, and provide inadequate analyses of uncertainty. In addition, little attention is paid to the positionality of the humans involved in the annotation process--both designers and annotators alike--and the historical and sociological context of skin tone in the United States. Our work is the first to investigate the skin tone annotation process as a sociotechnical project. We surveyed recent skin tone annotation procedures and conducted annotation experiments to examine how subjective understandings of skin tone are embedded in skin tone annotation procedures. Our systematic literature review revealed the uninterrogated association between skin tone and race and the limited effort to analyze annotator uncertainty in current procedures for skin tone annotation in computer vision evaluation. Our experiments demonstrated that design decisions in the annotation procedure such as the order in which the skin tone scale is presented or additional context in the image (i.e., presence of a face) significantly affected the resulting inter-annotator agreement and individual uncertainty of skin tone annotations. We call for greater reflexivity in the design, analysis, and documentation of procedures for evaluation using skin tone.