Secondary Data Modal-ity
Image Data
Crowdsource Annota-tions	Link to Data
https://storage.googleapis.com/mste/mste_annotations.csv	Data Fields Crowdsourced annotations can be found in a csv with the following headers (Header description follow the format Field Name, example, Description)
·subject, subject_0, Name of the subject
·image, PXL_0.jpg, Name of the image
·region, Ghana, Region where the annotation came from
·annotation, MST1, Crowd-sourced MST annotation.
Note that each image should have 5 annotations per region with a total of 25 annotations per image. Some images were too hard to annotate and were skipped by some annota-tors. In those cases there will be less than 25 annotations.
Purpose(s)
Fairness research	Domain(s) of application
Research, Fairness, Computer Vision, Skin Tone, Model Evaluation	Motivating factor(s)
·Subjects span the entire MST scale.
·Trains annotators to annotate images on the MST scale.
·Trains annotators to be invariant to lighting conditions.
·Supports fairness research.
Dataset Use(s)
Research
Annotator training
Model Evaluation	Intended and suitable use case(s)
·Research: Fairness re-search
·Annotator training: Can be used to train human annotators to annotate skin tone.
·Model Evaluation: Can be used to evaluate fairness in computer vision models.	Unsuitable use case(s)
·Model training: The images cannot be used to train machine learning models.
Version Status	Dataset version	Maintenance plan
Actively MaintainedNo new versions with new images will be made available, but this dataset will be actively maintained, including but not limited to updates to the data if errors are reported.	Current Version 1.0Last Updated 05/2023Release Date 05/2023	Storage: The dataset is stored on Google Cloud.Versioning: The website skintone.google/mste-dataset will contain the latest version of the dataset.Availability: The dataset will be available for download with a valid Google account. The dataset will remain available on Google Cloud.Feedback: Feedback and errors can be submitted through the Feedback form.
Provenance | Collection | Method(s) usedImages sourced through TONL.CO.Annotated by a human expert.Human attributesSkin Tone	Provenance | Collection | Methodology detail(s)Source: TONL.CO and skin-tone googleDate of Collection: 2022Primary modality: Image DataUpdate frequency: StaticSource(s) of human attributesSkin Tone: The images in the dataset were reviewed and annotated using the MST scale by the MST creator, Dr. Ellis Monk, an expert in social perception and inequality.	Provenance | Collection | Data ProcessingImages: The subjects, and subsequent images, were sourced through TONL.CO.Timing: All images and videos of a subject were collected within a short timespan of 4 hours to eliminate skin tone variation across images due to seasonal or other temporal effects.Rationale for collecting human attributesThe primary goal of this dataset is to enable and test consistent skin tone annotation across environment capture conditions. It provides a starting point to support practitioners to teach their annotators to annotate skin tone consistently on the fine-grained MST scale and a controlled set through which to study annotator behavior.
Distribution of human attributes
MST	1	2	3	4	5	6	7	8	9	10
Subjects Images Videos	2 179 2	3 218 5	2 112 5	2 187 3	2 179 4	2 165 3	1 76 4	2 143 2	2 142 5	1 111 1