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AMIT GANDHI: Hi, my name is Amit Gandhi.
And I'm a graduate researcher at MIT.
Welcome to this course on exploring fairness
in machine learning for international development.
In this video, we will examine bias
in machine learning models through a pulmonary health
diagnostic case study.
In particular, we will explore the influence
of representative data on accuracy when building a model.
Pulmonary diseases, including asthma COPD, allergic rhinitis,
and others, can have significant detrimental health impacts
if undetected.
In remote areas with limited access to health care,
they can often go undiagnosed and untreated.
The motivation for this work was to develop a screening
tool for community health workers
to determine if patients who were presenting symptoms
of pulmonary disease actually have pulmonary disease.
To develop the tool, data was collected
from 303 patients who sought medical care
at health clinics between 2015 and 2018 in Kuna, India.
Patient data was collected at health clinics
from two exams administered by researchers--
a mobile health diagnostic kit developed
by Dr. Fletcher's group and a set of measurements
from a pulmonary function test lab.
Health diagnoses were performed by medical staff
with a focus on asthma, allergic rhinitis, and COPD.
The overall disease distribution among the patients
is shown in the plot.
The data included 175 patients with pulmonary diseases
and 87 healthy patients.
Patients may also have multiple pulmonary diseases--
for example, asthma and COPD.
The exploration of representative sampling
on accuracy was conducted across two protected variables, gender
and income.
The population distributions for the two variables
can be seen in the slides.
For income considerations, patients
were categorized as either low income or high income.
The overall approach to the bias study
was to divide the data set into a larger training data
superset in a test data set.
A logistic regression model with L2 regularization
was used to make predictions on disease.
To train the model, training data subsets
were randomly sampled from the superset
that intentionally introduce imbalances
along protected variables.
For example, with regards to income,
training data subsets ranged from 50 percent 50%
and 50% low income to 87.5% high income and 12.5% low income.
To account for stochastic error, this process
was run 1,000 times for each test.
The area under the curve of the receiver operating
characteristic curve was used as a metric bracket for accuracy.
Starting with gender bias analysis,
our training data sets and test data set were divided as shown.
Male-female representativeness was varied from 50-50
to 87.5 to 12.5.
The results for predictive accuracy
for allergic rhinitis, asthma, and COPD
are shown on the slide.
The data shows no significant decrease in algorithm accuracy
as gender imbalances are introduced in the data.
This may be surprising considering
how we have highlighted the principle of representativeness
in data throughout this course.
However, it is important to note that protective variables do
not necessarily affect outcome variables
and the lack of representativeness
may not always introduce bias or fairness into models.
Looking at our results, we also notice
that our algorithm is more accurate at predicting
COPD in women than men.
Exploring the results further, we
look at other variables in the correlation with gender.
In our data set, we found that smoking heavily
correlated with gender.
55% of men reported that they were nonsmokers
whereas 100% of women reported that they were nonsmokers.
As a result, the population of women
was more homogeneous, allowing for higher predictive accuracy.
Moving on to the income bias analysis,
the training data sets and test data sets
were divided as shown.
Similar to the gender study, representativeness
based on income was varied for the training data set.
The results were predictive accuracy
for allergic rhinitis, asthma, and COPD
are shown on the slide.
Again, we see very little difference in accuracy
as we change representativeness within the sample.
COPD is the most sensitive to socioeconomic status,
with a 4% difference in model accuracy for high income
and low income populations.
Asthma and allergic rhinitis show
no difference in performance.
In summary, we found that representativeness
across the protected variables of gendered income
do not play a large role in model accuracy
for this example on pulmonary diseases in India.
As part of building a machine learning model,
it is always important to check what effects, if any,
attentive attributes may have on the model.
In the real world, it will be impossible to find perfectly
balanced data sets.
And test such as the one described
can be used to check for the effect of representativeness
across protected variables on data and model accuracy.
It is important to understand these tradeoffs
so that you can make informed decisions when building models.
Thank you for taking the time to watch this case study.
And we hope that you'll watch the other content
in the series.
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