The skin tone scale that scores 16.67% on an arm

If you build anything that reasons about skin, you almost certainly sort people onto the Fitzpatrick scale. A research team tried to do that automatically last year and scored 16.67% on a photograph of an arm.

The same team, on the same images, scored 65.67% on those arms when they expressed the answer on a different scale.

What the study found

A group at TU Munich built an algorithm that estimates skin tone from the Individual Typology Angle, computed from CIELAB colour values using DensePose and OpenFace. They mapped the same values onto two scales: Fitzpatrick, and the Monk Skin Tone scale Google published in 2022.

Same images, same pipeline, same colour measurement underneath. The only variable was which scale the answer was expressed in.

Balanced accuracy on clinical photographs:

Their wording on the Fitzpatrick results is blunter than mine: no clinically useful classification was possible.

Monk held steady across body regions and across light and dark tones. Fitzpatrick collapsed, and collapsed hardest on the arm, where there is no face shape to help the model guess.

Why it collapses

Fitzpatrick measures sunburn.

Thomas Fitzpatrick built it in 1975 to choose phototherapy doses. It works by asking a patient how their skin behaves in the sun. Do you burn, do you tan, how easily. It is a questionnaire about ultraviolet response with six possible answers, and for its original job it was a reasonable instrument.

It is now routinely used as though it described skin colour. The study above is itself evidence of that: it takes a colour measurement and maps it onto Fitzpatrick types, which is only a sensible thing to attempt if the field expects the two to correspond. The authors describe the scale as widely used and note that it is based on UV response, which is the tension they set out to test.

Those are different quantities. Two people with the same skin colour can honestly give different Fitzpatrick answers, and the scale has no mechanism to tell them apart, because nobody ever asked it to.

Monk was built to describe apparent skin tone, has ten points rather than six, and was designed knowing it would be used in computer vision. It is worth saying plainly that Monk is not evenly spaced either, and anyone treating its ten points as uniform steps will be wrong. What it has over Fitzpatrick is that it is measuring the right quantity.

What we tested

That study was an important input into what we built, so we evaluated before changing anything.

We used the Monk Skin Tone Examples set, the public dataset Google publishes alongside the scale, which carries ground-truth Monk values for every image.

The question was whether the paper's approach, computing tone from the colour measured in the image, survives contact with photographs taken on a phone.

The pixel route fails on phone photographs

Across 600 images, computing tone from pixels landed within one Monk step 32% of the time. In the light band it was right zero times. Almost everything collapsed into Monk 7 and 8 regardless of the person in the picture.

A phone camera automatically exposes for the face in frame. That is the entire job of the auto-exposure system, and it works: it pushes whatever the subject is toward a mid-tone so the photograph looks correct. A cheek belonging to someone at Monk 1 came back measuring around L* 52, which is the middle of the range.

So the camera is normalising away the exact quantity you are trying to measure, before your code ever sees the file. Any pixel-based skin tone pipeline running on consumer phone photographs is measuring the auto-exposure algorithm as much as the person.

Clinical studies use controlled lighting and calibrated capture. Someone holding their own phone in their own bathroom has neither, and that is the only condition that matters for a product used that way.

Going in, we expected the arithmetic to win. It is the auditable route and it is what the research uses. It lost in every band.

What changed in the product

Three things changed.

The measurement is Monk, and our analysis no longer emits a Fitzpatrick type at all. Where a Fitzpatrick value comes from a person, a trial participant or a clinician recording their own assessment, we keep it and label it with its source, because a self-reported answer to a self-reported scale is a legitimate thing to store. What we stopped doing is generating one.

We store the measurement, and derive everything else from it. The band and the friendly name a customer sees are computed at display time rather than written down as facts of their own, so any label can be traced back to the number that produced it.

Anywhere the system treats darker skin differently, it now asks one question in one place. Rules of that kind accumulate copies, and the copies disagree. There was a tone sitting right on the boundary that one path included and another missed.

What it changes for the person in front of the camera

Guidance that depends on skin tone now reaches everyone it should. Several things in skincare are handled differently for deeper skin, and those rules used to ask their question in more than one place. The answers did not always agree, so somebody sitting near the boundary could be covered by one path and missed by another. There is a single definition now, and it is applied everywhere the question comes up.

Advice that claims to be tone-specific now is. A six-point scale with a gap in the middle pushes a wide band of people onto the same value, and when most people share a value, personalising by skin tone produces the same answer for all of them. A ten-point scale measured rather than guessed gives the rest of the system something real to vary on.

The range now covers people it previously did not. The old scale had nothing above its lightest point and a sizeable gap in its middle, so anyone falling in either was moved to the nearest available label whether it described them or not.

What nobody should be claiming

If a product reports a Fitzpatrick type, it owes you three things: which scale, measured how, and how the method performs at the dark end specifically rather than on average.

A systematic review of 232 dermatology AI studies found that only 1.3% described the Fitzpatrick skin type of their subjects at all, and only 3.2% of the images were types IV to VI. The same literature keeps finding that AI detects skin conditions less accurately on darker skin. So the population most affected by the accuracy gap is also the one least represented in the work measuring it.

Until that changes, "works across all skin tones" tells you that somebody checked, and nothing whatsoever about what they found.

Study: Ulrich P, Zink A, Biedermann T, Sitaru S. Beyond Fitzpatrick: automated artificial intelligence-based skin tone analysis in dermatological patients. npj Digital Medicine, June 2025. Review of 232 studies: Jairath et al., Dermatologic Surgery, 2024, via this narrative review. Evaluation dataset: Monk Skin Tone Examples.