Methodology Overview (less than 1 page)
Our team's approach to measuring and mitigating bias was informed by commonly used
mathematical and statistical approaches, our experience developing machine-learning
algorithms, and our experience managing, measuring and transforming data to the FHIR
standard for 40M patients at 1upHealth.
To measure bias, our team approached the problem by considering the principles of
equal opportunity, equal odds and differential validity. This approach allowed us to most
effectively measure retrospective and latent bias present in the output of a given model. If our
tool is used to measure a model’s output over time as it is updated with newer data, we would
also be able to measure the change in bias over time.
We utilized demographic data in conjunction with the provided binary outcome data to
measure bias across the intersection of two group categories: sex/gender and race. While we
used binary outcome data to produce these plots, continuous outcome data could also be used.
We calculated the following metrics:
● Equal odds difference
● Equal odds ratio
● Demographic parity difference
● Demographic_parity ratio
Our solution supports output from all classification models including classical linear models, svm
tree and XGboost type models, neural networks, and sequence to sequence generative AI
models. We tested our bias measurement tool with multiple models which included biased, poor
performance, excellent performance, and random guess output. Through these tests and by
using our own generative AI model we were able to test and develop the above bias
measurement criteria.
To mitigate bias, we created a classifier using XGBoost in combination with a threshold
optimizer. The classifier is trained to predict a binary outcome based on a column defined in the
input dataset. The model requires a number of arguments to be supplied in order to define the
protected classes and reference classes.
The model also uses a threshold optimizer from Fairlearn in order to adjust the sample weights
to minimize the equalized odds between the protected and reference classes. In other words, it
is trying to match true positive and false positive prediction rates across all classes as defined in
the input.
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