SlowGuess/NeurIPS_2023_OCR huggingface dataset:
FST also better represents a range of lighter skin tones than a range of darker skin tones [27, 29]. One study found that only  $42\%$  of an ethnically diverse group of participants could be classified using the standard Fitzpatrick scale definitions [23]. Sommers (2019) [78] summarized it saying, "they [self-reported Fitzpatrick Skin Type] provide a restricted range of options for people with darker skin that does not capture variations in their skin color." This highlights a core limitation of the FST; it is not inclusive of the full spectrum of skin tones. This limits the ability for the FST to faithfully represent people, and the ability for systems built upon human annotation to be fairly evaluated across skin types.
Researchers have called for alternative and finer-grained skin tone measurements both within dermatology [66] and more broadly in fairness for computer vision [59]. The ability to draw on a finer-grained scale that reflects a broad range of skin tones provides flexibility to support careful design choices for disaggregated evaluations [3] and can play a crucial role in addressing performance disparities, especially those which disproportionately impact communities that have faced injustices and have been historically underrepresented [8]. Although finer-grained measurement is the goal, it is not without its challenges and limitations.
Skin tone scales with too many shades, like the 36-point Felix von Luschan Skin Color chart [80], can introduce additional cognitive load for annotators during an already challenging task, potentially reducing inter-rater agreement. In addition, it may be difficult for annotators to identify differences between skin tone shades at such fine-grained measurement, leading to lower annotator accuracy compared to an expert ground truth assessment.
We have chosen to have annotators use the 10-point Monk Skin Tone (MST) scale [59] (Figure 1). The MST scale was designed through its color selection and gradations to optimally capture ethnoracial diversity in skin tone across the Americas (e.g. North, South). This includes capturing skin tone variation within and across racial/ethnic categories in the United States (and potentially beyond). At 10 points, we hypothesized MST would give annotators a broader and more diverse range of skin tones compared to the FST without overwhelming annotators with too many options. Although a finer-grained scale adds more complexity and difficulty to achieve consensus at the annotation stage (compared to, for example, annotations for a binarized skin tone annotation task), we hypothesized that the MST allows for a more nuanced understanding of system behavior, better targeted disaggregated evaluations and audits, and more capable measures of representation.
A.3 Annotations for ML Fairness in Computer Vision
Perceived signals such as gender expression, skin tone, and age are commonly used in fairness evaluations and bias mitigation techniques in computer vision [82, 8, 84]. There are four main methods currently used in collecting these annotations: third party crowdsourced annotations, annotations from captions, self reported annotations, and algorithmic or model generated annotations.
Crowdsourcing annotations for fairness evaluations is extremely common in computer vision. Zhao et al. [85] collect both Fitzpatrick Skin Type [26] and gender expression using Amazon Mechanical Turk (MTurk) with three replications per image. Amazon Mechanical Turk (AMT) is a popular crowdsourcing platform that is commonly used across both computer vision and general machine learning practitioners. Difallah et al. [21] found that the majority of MTurk workers are based in the US and India. They also found that MTurk workers tend to be younger than the overall population. Buolamwini and Gebru [8] collected Fitzpatrick Skin Type annotations for each individual through a board-certified surgical dermatologist. This type of annotation is not scalable to larger datasets. Schumann et al. [77] show how annotation pipelines can introduce biases into annotations. They also provide a dataset Open Images Extended: More Inclusive Annotations for People (MIAP) with crowdsourced annotations from annotators in India of perceived gender presentation and age range. In this paper we focus on crowdsourced annotations for skin tone across a wide geographic region.
Captions are often used to provide fairness annotations for images. For example, a body of work on investigating and mitigating bias in image captioning builds on the dataset and definitions introduced by Zhao et al. [86]. Here, images from MSCOCO are filtered into dataset slices depending on whether
the ground truth captions include only the word "man", or only the word "woman". Images with captions mentioning both terms are excluded. Recent work on understanding gendered bias in vision datasets [57] adopts a similar strategy to identify dataset slices, with updates introduced by Zhao et al. [85] that expand the list of gendered terms included in the filters. It is possible to construct sliced datasets of reasonable scale using this procedure because it is common for descriptions of people in the source datasets to use gendered terms. In fact, the annotation instructions in the Localized Narratives dataset include the example "a man is flying a kite" [68], implicitly encouraging this practice.
In contrast, we find that captions in existing datasets rarely include description of an image subject's skin tone. We used Know Your Data<sup>5</sup> to find and manually inspect images containing a person bounding box and captions that contain the word skin or skinned. Out of all 123,287 images, we find just 12 images with captions describing skin tone - one "pale skin" and 11 "dark skin(ned)". Given that recent developments in large-scale vision-language models rely on paired text from web-crawled alt-text [13, 75] rather than curated captions, we consider whether alt text datasets may exhibit different properties. Hanley et al. [36] provides alt text guidelines from two museums that recommend describing skin tone in images, and notes that nearly all of the concrete examples include these descriptors. However, web-crawled alt text from generic sources is of varying quality and does not adhere to these standards [6]. Even if we could ensure museum-quality alt text that follow these published guidelines, the suggested language simplifies the concept, resorting to more binarized language like light- or dark-skinned.
Liu et al. [51] released the Casual Conversations Dataset which includes both self-reported gender and age. In contrast, the authors of the dataset use third party annotations of Fitzpatrick Skin Type, not self-reported values, to categorize the skin tone of participants. Datasets with self-reported annotations are expensive to collect and tend to be very "clean" and not in-the-wild images or videos. There is also an important distinction between subjective attributes such as skin tone, non-subjective attributes such as gender identity and age, and the perception of these attributes. While self-reported values are extremely useful and important Liu et al. [51], the choice to use self-reported values vs third-party annotated values depends on the perspective practitioners want to understand: a) how models interact with the identities and non-subjective attributes of users or b) how models perform on different perceived attributes across a wide range of imagery (including generated imagery).
Finally, fairness annotations can be obtained through models as explored by Buolamwini and Gebru [8]. However, these models need to be trained on annotations acquired from one or more of the above techniques. Skin tone measures based off of ITA are notable exceptions. These measures are computed from pixel values from detected skin regions in the images with the objective of matching physical measurements captured under controlled lighting conditions [19, 42, 73]. Raw pixel values in images captured under unknown lighting conditions are a function of both the scene lighting and the subject's true skin tone. Earlier work recognizes but does not always attempt to correct for these effects [43, 45]. Others apply color correction by leveraging known properties of the scene or capture environment [48, 42]. Recently, Feng et al. [25] introduce a learned method that jointly models scene illumination inferred from scene context and facial albedo, from which ITA can be computed. Cho et al. [14] present a rare instance of a method that estimates skin tone directly from pixel values but bypasses ITA computation. They detect skin regions in images and select the Monk Skin Tone category that most closely matches the average RGB value of pixels within this mask. We do not recommend this approach for MST annotations. See Appendix G for an illustration of limitations of this approach with unconstrained images.
B MST-E Golden and Challenging Image Sets
The MST-E dataset includes a set of Golden Images where each subject is represented by an image that best represents their skin tone. A Challenging Images set was also selected with worse lighting conditions.
<sup>5</sup>https://knowyourdata-tfds.withgoogle.com/#dataset=coco_capitals
Golden Images
Challenging Images
C Data Card
Publishers
Google LLC
TONL.CO	Team
Google Research, Perception and Responsible AI teams	Contact Detail
Contact: Feedback form
Website: skintone.google
Data Subject(s)
Data about image search queries	Data Snapshot
Dataset size 5.21 GB
Subjects 19
Images 1515
Videos 31	Data Description
The 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.
Images by TONL. Copyright TONL.CO 2022 ALL RIGHTS RESERVED.
Primary Data Modality
Image Data Video Data
Link to Data
skintone.google/mste-dataset
Data Fields
Data field for each image and video can be found in a csv with the following headers (Header descriptions follow the format Field Name, Example, Description)
- image_name, PXL_0.jpg, Name of the image
- pose, side, Pose of the subject
- lighting, well_lit, Lighting condition of the image
- mask,false, Whether the subject is wearing a mask
- subject_name, subject_0, Name of the subject
- MST, 1, MST annotation for the subject
The Golden Image Set and Challenging Image Set can be found in a csv with the following headers:
- subject, subject_0, Name of the subject
- golden_image_id, PXL_0.jpg, Name of the golden image
- challenge_image_id, PXL_1.jpg, Name of the challenging image
The Images and Videos for each subject can be found under the subject's folder. For example, in the above examples image  $PXL\_0.jpg$  can be found in the subject_0 folder.
Annotation workforce type
Human Annotations - Expert	Annotation characteristics
Number of unique annotations: 19	Annotation description
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. Each subject was annotated with a single MST-E value.
Concepts and definitions referenced in this data card
MST
Definition: Monk Skin Tone
Source: skintone.google
Interpretation: Inclusive 10 point skin tone scale.
Annotation workforce type
Human Annotations - Crowdsource	Annotation characteristics
Number of unique annotations:	Annotation description
Crowdsourced annotations for the MST-E dataset has been made available for replication purposes only. Each image was annotated 5 times in 5 different regions totalling 25 annotations per image.
D Annotator Experience
Pick the skin-tone that matches most closely.
Which part of the person's body did you primarily use when making your selection?
How easy or difficult was it to assess this person's skin-tone?
The figure above shows the annotation task as seen by the professional photographers in India and the US. The task was presented in English and all photographers were fluent English speakers. The photographers were sourced through GLG, Gerson Lehrman Group. Each network member of GLG, including the photographers from this study, accept the GLG Terms and Conditions prior to participation.
The figure above shows the annotation task as seen by the trained crowdsourced annotators located in India, the Philippines, Brazil, Hungary, or Ghana.
E Professional Photographers' Regional Differences
Figure 7 shows the complete Mann Whitney U tests comparing annotations from professional photographers in the US to professional photographers in India. It includes the tests over the Golden Images set (Figure 7(a)) and the Challenging Images set (Figure 7(b)).
MST	Subject	Value	p
MST1	S18	7.5	0.179712
MST2	S1	19.5	0.147499
MST2	S13	23.0	0.026888
MST2	S8	18.0	0.256839
MST3	S0	18.5	0.218758
MST3	S15	20.0	0.070701
MST4	S7	12.5	1.000000
MST4	S9	21.0	0.058758
MST5	S11	14.0	0.796253
MST5	S6	11.0	0.824848
MST6	S14	24.0	0.018119
MST6	S3	8.5	0.408308
MST7	S5	9.0	0.488422
MST8	S17	9.0	0.512412
MST8	S2	6.5	0.204024
MST9	S10	12.5	1.000000
MST9	S4	6.0	0.175151
MST10	S12	9.5	0.588809
(a) Golden Images
MST	Subject	Value	p
MST1	S18	13.0	1.000000
MST2	S1	18.0	0.248213
MST2	S13	13.5	0.910670
MST2	S8	14.5	0.733730
MST3	S0	20.0	0.132622
MST3	S15	9.5	0.587594
MST4	S7	19.5	0.154424
MST4	S9	16.5	0.443194
MST5	S11	20.5	0.110492
MST5	S6	13.0	1.000000
MST6	S14	22.0	0.048632
MST6	S3	6.0	0.188667
MST7	S5	13.0	1.000000
MST8	S17	4.0	0.058758
MST8	S2	6.0	0.193059
MST9	S10	10.0	0.607236
MST9	S4	8.0	0.344659
MST10	S12	11.0	0.817361
(b)Challenging Images
Figure 7: Mann-Whitney U testing the difference in the annotations of professional photographers in the US versus India. Figure 7(a) tests annotations over the Golden Images set. Figure 7(b) tests annotations over the Challenging Images set.
F Distance from Intended Annotations
Figure 8 shows confusion matrices of Dr. Monk's annotations compared to the median annotations by the crowdsourced annotators.
G Pixel-Based Skin Tone
As mentioned in the related work, measuring skin tone from pixel values has limitations (§2). In this experiment we determine the skin tone through a) automated skin tone detection and b) manually cropped images. For each of these methods we look at closest MST skin tone by averaging all skin tone pixels and finding the closest MST point based on the L2 distance in RGB space to the reference colors [14].
G.1 Automated skin tone detection
Following Cho et al. [14], we mask out non-skin tone pixels using [47]. We find that this method performs poorly as it picks up some background pixels, and does not capture all of the face - particularly dark skin tones (see Figure 9).
Given that this method does not pick out skin tone well, the closest MST values do not accurately represent the subjects skin tone (See Figure 10).
G.2 Manually cropped images
In addition to the automated skin selection above, we also manually drew boxes around an open portion of skin, and used the pixels from that crop (See Figure 11). As seen in Figure 10, cropped images with only skin tone pixels still do not produce accurate MST annotations.
H Maintenance plan
The dataset will be stored on Google Cloud indefinitely. The website skintone.google/mste-dataset will remain updated with the latest version of the dataset. The dataset will be available for download with a valid Google account. Any feedback or found errors in the dataset can be reported through the skintone.google website feedback form. Any errors will be fixed and the dataset will be updated accordingly. Currently we do not plan on releasing a V2.0 version of the dataset.
I Author statement and dataset use
Each subject provided consent for their images and videos to be released. TONL has given permission for these images to be released and used for research or annotator-training purposes only (Copyright TONL.CO 2022). The images cannot be used to train machine learning models. Annotations provided with the images can be used for research or annotator-training purposes only. When using the dataset for research, please cite this paper and credit TONL for any images used.
The authors of this paper bear all responsibility in case of any violation of rights during the collection of the data or other work, and will take appropriate action when needed, e.g. to remove data with such issues.
J Dataset format
The full dataset can be downloaded from skintone.google/mste-dataset. The download will be a zip folder containing images in . jpg format, videos in .mp4 format, and annotations in a .csv format. See the datacard in Appendix C for more information about the fields found in the .csv file.
(a) Golden Images Set
(b)Challenging Images Set
(c) Annotations per Subject
Figure 8: Comparisons between the intended MST value as annotated by the MST creator and the median value selected by crowdsourced annotators across subsets of images and different annotator pools.
(a) Golden Images
(b)Challenging Images
Figure 9: Detected skin tone following [47]. All pixels that are not skin tone are black.
K Annotator compensation
The compensation rates for the expert photographers and the crowdsourced annotators were dictated by the relevant sourcing vendors. Payment rates are determined by fair market value in the different regions. The expert photographers were paid a token honoraria not exceeding $500 by GLG for participation in two 30 minute surveys. Crowdsourced annotators were contracted by the sourcing vendor to work for monthly increments. The rough monthly payment for crowdsourced annotators per region is as follows:
Location	Per Rater/Mo	Estimated Hourly Wage
India	~$1,100.00	~$6.00
Philippines	~$2,000.00	~$11.00
Hungary	~$3,500.00	~$19.00
Ghana	~$2,300.00	~$13.00
Brazil	~$2,300.00	~$13.00
Overall, we spent approximately  $780k on crowdsourced annotations. The total compensation for the group of expert photographers who provided annotations through GLG was approximately$ 5000.
L Addressing previous reviews
This paper was previously submitted to FAccT 2023 titled, Consensus and Subjectivity of Skin Tone Annotation for ML Fairness. The reviewers agreed on the paper's relevance noting the nuance and exploration of skin tone annotation across regions and the implications of the work for practitioners, but also noted some objections and opportunities in their rejection of the submission. We leveraged their feedback for a stronger submission, and below is a summary of their feedback and how the feedback was addressed in this submission.
Monk Skin Tone Scale In the prior submission reviewers raised objections about a missing citation for the Monk Skin Tone scale and who was chosen as a Monk Skin Tone scale expert to provide ground truth annotations. Due to the timing, we were unable to cite the now published Monk Skin Tone scale white paper that describes the scale's development. We have cited it in this paper in reference to the scale.
(a) Automated Skin Tone Detection
(b) Manually Cropped Images
Figure 10: Actual MST annotations by the MST creator compared to the closest MST RGB from the average RGB of the detected/selected skin tone pixels.
Relatedly, one reviewer raised a question about the effect of scale length and why a 10-point scale is better or worse than other compositions. This is an interesting question, but out of scope for this paper. In this submission, and the prior one, we focus on people's ability to annotate skin tone on this particular scale due to the recent fairness literature adopting the Monk Skin Tone scale.
Monk Skin Tone Scale Expert In the prior submission reviewers raised objections about the unidentified Monk Skin Tone scale expert, (described as “a Monk Skin Tone scale expert”), and what credentials they possessed to establish ground truth on skin tone, a characteristic we highlight as subjective. In the previous submission we wanted to remain double blind, but in this submission we highlight that Dr. Monk is the Monk Skin Tone scale expert and cite his credentials of academic expertise in skin tone research and the creator of this particular skin tone scale.
Expert Annotation Exploration In the prior submission reviewers raised questions about the selection and limited sample size of experts in the expert annotation portion of the paper. In this submission we clarify why photographers were chosen instead of dermatologists – the type of expert chosen in similar previous research. Photographers tended to assess skin tone in the image while some dermatologists tended to devise the skin tone under clothing which was not depicted in the image (or skin untouched by sun). We also more clearly delineate the expert portion of the paper as exploratory and use the results from this work to establish hypotheses for the properly powered
(a) Manually Drawn Box
(b) Resulting Crop
Figure 11: Manually drawn box on an open selection of skin, and the resulting crop of the manual selection.
analyses later in the paper. Finally, we also note an opportunity to more expressly test annotation across various types of experts in the limitations for future consideration.
Analytic Clarifications In the prior submission reviewers sought some additional clarifications in the analyses. At a high-level, we separated the analyses by sample in this version of paper to better delineate the separate analyses, and increase the clarity of the results. We also more clearly define "average median distance" and describe the "1-point discrepancy" threshold citing how they were used in previous research and their purpose in this work. Finally, we made some adjustments to the figures and footnotes where reviewers noted confusion or a preference for more information.
Clarification of Scope In the previous submission we reviewed a broad scope of research including subjective and objective forms of skin tone annotation and in this submission we more clearly focused on subjective annotations to streamline the literature review and highlight the particular focus of this contribution.
Limitations and Ethical Considerations The previous submission did not include an explicit section dedicated to the limitations and ethical considerations of the submission. In this submission we included both to highlight further opportunities to investigate annotation ability quality and the proper usage of the MST-E dataset.
Monk Skin Tone Scale-Examples Dataset One reviewer noted a limitation of the MST-E dataset, it only includes 19 subjects. This is a limitation we accept and acknowledge in the paper, but we are unable to address in this iteration of the dataset. Another reviewer mentioned some additional demographic details like race/ethnicity of the MST-E subjects, but this data was explicitly not collected from the models. Finally, a reviewer mentioned that releasing the annotations would increase the utility of the MST-E dataset for practitioners if we were able to make those publicly available as well. We release the annotations with this paper. You can download the annotations from https://storage.googleapis.com/mste/mste_annotations.csv. See Appendix C for more information.