Society GUIDE
Skin Tone Bias and the Monk Skin Tone Scale
The Monk Skin Tone (MST) Scale is a 10-shade visual scale developed by Ellis Monk with Google and released in 2022 to help represent skin-tone variation in technology evaluation and annotation.
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Overview
It can improve coverage compared with narrower scales, but it is not a race classifier or clinical diagnosis, and image lighting, display calibration and annotator judgment still affect measurements.
Deep Dive
Google and Harvard sociologist Ellis Monk released the Monk Skin Tone Scale in 2022. It provides ten visual shades intended to represent a wider range of human skin tones for technology research and product development. The scale can support dataset annotation, image search evaluation and subgroup analysis where teams previously used coarse categories or the Fitzpatrick Skin Type scale as a proxy for visible skin color. The scale is openly available for research and product work; it is not a government standard or legal requirement.
The MST addresses a measurement problem, not all causes of racial or color bias. A 2024 study comparing Fitzpatrick, Fenty and Monk scales surveyed 2,214 people in the United States and found the Fitzpatrick scale was perceived as less inclusive than the other two, especially by darker-skinned and historically marginalized participants. Google’s annotation research found that skin-tone labeling can be subjective and studied how annotator experience affects agreement. Skin tone in an image also depends on lighting, camera processing, display characteristics and the image itself. A ten-shade scale therefore provides useful common reference points, not perfect measurement or a substitute for self-identification.
Researchers should distinguish skin tone from race, ethnicity, ancestry and medical response to ultraviolet exposure. The Fitzpatrick scale was originally designed around burning and tanning response, not as a universal image-skin-tone metric. Using it as a skin-color proxy can cause mismatch. The MST is likewise not designed to diagnose skin disease or assign a person to a racial group. When evaluating AI, teams should state what the label measures, who selected it, how image capture was standardized and how annotation uncertainty is handled.
Strategic Impact
Risk and safety
Catastrophic and everyday AI harms both depend on who understands the risks and who can act.
Clearer decisions
Public and professional literacy shapes whether strong safety policy is politically possible.
Cutting through hype
Clear explanations reduce capture by hype, lab PR, and vague ethics theater.
The Future of Skin Tone Bias and the Monk Skin Tone Scale
The MST is an open resource that can evolve as evaluation practice improves. Newer research continues to compare subjective scales with calibrated color measurements. Teams should document version, annotation protocol and capture conditions, and update audits when evidence shows the labels do not represent the affected population well. Keep a dated record of the primary source or study behind each claim and revisit conclusions when new evidence or implementation details emerge. Community feedback and colorimetric tools can help identify where visual categories fail to capture lived variation.
Real-World Implementation
A computer-vision team samples images across the ten MST shades when auditing face detection rather than relying on one light/dark split.
A dataset curator trains annotators with reference examples and records how ambiguous skin tones are handled.
A medical researcher avoids substituting the MST for a clinical skin-phototype instrument without validating that use.
A product evaluator checks color calibration and image capture conditions before comparing model performance across skin-tone labels.
Risks & Guardrails
Treating existential risk as sci-fi while capability compounds.
Confusing surface product safety with alignment under high autonomy.
Leaving non-English and non-expert audiences with only low-quality sources.
Implementation Roadmap
Separate product harms, misuse, and loss-of-control / misalignment risks.
Ask what evidence would change your view on timelines and severity.
Prefer primary sources and concrete evals over marketing claims.
Identify one action path: career, policy, funding, or skills — not only awareness.
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Frequently asked questions
What is Skin Tone Bias and the Monk Skin Tone Scale?
The Monk Skin Tone (MST) Scale is a 10-shade visual scale developed by Ellis Monk with Google and released in 2022 to help represent skin-tone variation in technology evaluation and annotation. It can improve coverage compared with narrower scales, but it is not a race classifier or clinical diagnosis, and image lighting, display calibration and annotator judgment still affect measurements.
How many shades are in the Monk Skin Tone Scale?
The MST is a ten-shade scale developed for more inclusive representation of skin tones.
Which task is the MST Scale intended to support?
The scale was released to help represent skin-tone variation in technology and product development.
Which researcher developed the scale in partnership with Google?
Google released the scale in partnership with Harvard sociologist Ellis Monk.
Does an MST category identify a person’s race or ethnicity?
The scale describes skin-tone shades; it does not classify race or ethnicity.
What did the 2,214-person comparison study find about the Fitzpatrick scale?
The study compared three measures and found the Fitzpatrick scale less inclusive in participant perceptions, particularly among darker-skinned and marginalized participants.
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