Hi, I'm Matt Groh, and I'm a finalyear PhD student at the MIT media Lab.I'll be presenting
research today on how we canincrease transparency in the machine learning development pipeline
suchthat we can avoid systematic errors in algorithmic decisions,support tools, and in
particular address how we cando this for large data sets of images ofskin disease that are
used for trained models toclassify skin disease.So this is joint work with Caleb Harris, Dr.Roxanne
Dinesh Chao, Dr.Omar Medri and Dr.Arash Kuchak, and it's at theintersection of computer supported
collaborative work,machine learning and dermatology.Also, I want to mention upfront that this presentationwill
include some clinical images of skin disease.So let's get started.AI systems hold promise
for aidinghealthcare professionals in clinical diagnosis tasks.For example, in a paper published
in 2017in Nature, researchers showed that convolutional neural networksin a very specific
setting can classify skinlesions as the nine or malignant at similarrates to board certified
dermatologists.And there's another article in Nature Medicine in 2020.So three years
later, two years ago, thatdemonstrated a deep learning system based on acouple of images
and some metadata can classifyskin disease at a similar rate to boardcertified dermatologists,
and significantly more accurate than primarycare physicians or nurse practitioners.However,
the Nature paper did not report details ondemographic or skin tone information about the images
themodel was trained or evaluated on, and in theNature of Medicine paper, they did report
these details,but only included less than 3% of people withthe darkest two skin types.In
a recent review in JAMA Dermatology, researchersrevealed that a vast majority of the datasetsused for
machine learning and dermatology do notactually contain skin tone labels.And in a recent consensus
statement published inthe same journal, researchers came up with achecklist for evaluating imagebased
AI, which includes onepoint on skin tone information and procedure whichskin tone was assessed
by.Now, the most common method for incorporating skintone information in algorithmic audits
is using theFitzpatrick skin type scale, which I mentioned earlierwhen I was referring to
the two darkestskin types in the Nature Medicine paper.I'm presenting a screenshot from our
paperwhich shows the Fitzpatrick skin type scaleas it was published in 1988.And what
I want to do is note thatthe original scale does not include nuanced skin tonesor color
beyond white, brown and black.And although Fitzpatrick skin type scale is highlycorrelated
with an individual's melon index, which canbe measured by narrowband spectrophotometric
devices, the scaleis a subjective measure ultimately and in clinicalpractice, which
is generally in person.Clinical fibs factor skin types are visuallyassessed by dermatologists
based on colors ofa patient's skin, hair and eyes, andmost importantly, their history of
sunburns.As researchers Joy Boulawini and Tim McGaw pointed outin their gender shades
paper, fitzpatrick skin type servesas a starting point for algorithmic audits, but it'simperfect
given the coarseness of its categories.Now, to give you a sense of what theselook like,
here's one example of Acne vulgaris labeledas its factory skin type one by crowd labellers.And
here's an example ofcontact dermatitis labeled as three.And here's an example of pediatrics
rosalabeled as pizza skin type six.Now, given that there's some degree of subjectivity ofestimating
pittpatrick skin type from an image, one questionis who has the authority to do so?Who is qualified
to do so?Should this be done by a dermatologist?Could this be done by an algorithm at thesame level
of agreement as a board certified dermatologist?And what about crowd workers?How would they compare
toan algorithm or expert dermatologist?So we asked three board certified dermatologists
to label 320textbook images of skin disease and 160 images from theFitzpatrick 17K data
set, which is an open and availabledata set for people to do research on.We also asked
two companiesspecializing in crowdsource annotations.Those two companies are scale AI and CenterLabs
to annotate both the textbook and Fitzpatrick17K images with their dynamic consensus protocols.And
they labeled almost or annotatedalmost 5000 annotations through this process.Now, we also
applied an algorithm called IndividualTypology angle or Ita to these images.Ita is a commonly
used algorithm in this space.And what I mean by commonly used is there arepublished papers
that use ita to estimate skin type.And ita is a computer vision algorithm thattransforms
RGB values in an image into asingle metric for pigmentation of the image.And we specifically
use a YCbCr mask.And what that does is it masks out non skinlike pixels in an image
such that you can avoidclothing or backgrounds that are otherwise not skin oriented.In fact,
it is is best done on segmentedimages, but generally it's hard to get those segmentations.So
we're doing ita on a masked image, whichis slightly better than Ita without the mask.So
before I compare the experts, crowdsand algorithm altogether, I want to sharehow experts compare
to each other.So here's a qualitative look where we'veplotted images based on two experts
annotations.And as you can see, there are examples where expert onelabeled an image
with this Patrick skin type four, whereas experttwo labeled it with a one and vice versa.For
certified dermatologists, the experts in theirfields will not necessarily annotate the sameimage
with the same score.Here's a confusion matrix showing the same two dermatologistsover the
320 textbook images that they labeled.And what we see is that annotations between twodermatologists
line up exactly in about 50% of images.That's about half of images that you're getting the
exactsame annotation out of the six categories that you mightchoose, and a 7th category being
not applicable.But if you take a closelook at the confusion matrix here.You'll notice
that the expertsrarely disagree very much.In fact, expert annotations are within one unitof
each other in 93% of the images.So, when we compare the three experts to crowdconsensus
protocols in an algorithm by their Pearson correlationcoefficient, we get the following
heat map and asyou look at this, you'll notice that the Pearsoncorrelation coefficient is
in the range for experts andthe crowd consensus protocol when you compare all thoseto each
other.But it's only in the zero 65 rangewhen you compare the algorithm to either theexperts
or the crowd consensus protocols.
In the paper.We conduct tested statistical significance and we don'tfind a statistically
significant difference between expert labelsand crowd consensus labels, but we do finda statistically
significant difference between algorithmically produced labelsand the other methods labels.So
in addition to the Pearsoncorrelation coefficient, we can evaluate iterativereliability with
Cohen's Kappa.We see the same results when usingquadratic rates for calculating the Kappa
score.And just want to mention that this was a shortintro to the paper, but if you check
out thepaper you can learn a lot more about it.We'd be excited to hear your feedback on
it.And I want to mention a special thanks toScale, AI and Centaur Labs and all the crowdworkers
who participated in this experiment and the collaboratorswho participated for making this
work possible.So thank you very and feel free to reach out.