Overview

GenHealth's submission to the NIH NCAT's Bias Detection Tools in HealthCare Challenge

Setup

Here's how to setup the project locally:

We recommend using virtualenv to create a local environment for this project. The following steps will create a virtual environment and install the required packages:

virtualenv -p python3.10 genhealth_bias
source genhealth_bias/bin/activate
pip install -r requirements.txt

Running the Scripts

Measure Disparity

The measure_disparity.py script by default expects a CSV file as input. For convenience, we have included a demo CSV file which includes diabetes admission and treatment information in the demo directory:

The script can be run with the following command line arguments:

Name	Description	Expected Values	Example
probability-col	A column containing the model's predicted probability of the outcome being true (1)	float between 0 and 1	0.7
binary-outcome-col	The binary outcome predicted for the row	0/1 or y/n or true/false	true
pos-outcome-indicator	The label indicating a "true" value from the binary outcome column. (Case sensitive)	string	1
sample-weights-col	The sample weights	float	5.2
protected-classes	A comma separated list of columns containing protected classes	pro1,pro2,pro3	sex_f,race_african_american
reference-classes	A comma separated list of columns containing reference classes	ref1,ref2,ref3	sex_m,race_white
input-file	The location of the input file	An absolute or relative path to a file	demo/compass_data_with_predict_probability.csv
debug	Enable additional debug logging	True/False	True

Example:

python measure_disparity.py  \
    --probability-col=predict_probability \
    --binary-outcome-col=readmitted \
    --protected-classes=race_AfricanAmerican,race_Asian,race_Hispanic,gender_Female \
    --reference-classes=sex_Male,race_Caucasian \
    --input-file=demo/diabetes_with_predict_probability.csv \
    --pos-outcome-indicator=1 \
    --reference-classes=race_Caucasian,gender_Male

Expected Output:

| protected_or_reference   | class                |   num_samples |   equal_odds_difference |   equal_odds_ratio |   demographic_parity_difference |   demographic_parity_ratio |
|--------------------------|----------------------|---------------|-------------------------|--------------------|---------------------------------|----------------------------|
| protected                | race_AfricanAmerican |         19210 |                   0.044 |              0.923 |                           0.034 |                      0.934 |
| protected                | race_Asian           |           641 |                   0.107 |              0.705 |                           0.128 |                      0.738 |
| protected                | race_Hispanic        |          2037 |                   0.052 |              0.857 |                           0.039 |                      0.919 |
| protected                | gender_Female        |         54708 |                   0.005 |              0.993 |                           0.006 |                      0.987 |
| reference                | race_Caucasian       |         76099 |                   0.025 |              0.961 |                           0.001 |                      0.998 |
| reference                | gender_Male          |         47055 |                   0.001 |              0.997 |                           0.007 |                      0.986 |

Mitigate Disparity

The mitigate_disparity.py script by default also expects a CSV file as input. For convenience, we have included a demo CSV file demo/diabetes_with_predict_probability.csv which can be used to test the output.

Name	Description	Expected Values	Example
input-file	The absolute or relative path to the train file	directory + filename	./demo/diabetes.csv
test-file	The absolute or relative file to the test file. If no test file is defined, the code will divide the input file into a train/test split	directory + filename	./demo/diabetes.csv
protected-classes	Comma separated list of sensitive classes	string	race_AfricanAmerican,race_Hispanic
reference-classes	Comma separated list of reference classes	string	race_White,sex_M
binary-outcome-col	Column containing binary outcome data on patient	0/1 or y/n or true/false	1
use-pos-weights	Use autogenerated positive weights. Note: This will ignore the sample-weights if those are specified.	True or False	True
sample-weights-col	Sample weights column. Note: specifying use-pos-weights will cause this to be ignored.	string	sample_weights
do-hyperparameter-optimization	Optional flag to perform hyperparameter optimization	True or False	True
train-test-split	Train/test ratio split of data. Default's to 0.8	Float	0.7
random-state	Seed for initializing the random state. Default to 0	Integer	2
pos-outcome-indicator	The label indicating a "true" value from the binary outcome column. (Case sensitive)	string	1
debug	Enable additional debug logging	True/False	True

To run the demo script with a custom file, use the following command:

python mitigate_disparity.py \
    --binary-outcome-col=readmitted \
    --protected-classes=race_AfricanAmerican,race_Asian,race_Hispanic,gender_Female \
    --input-file=demo/diabetes.csv \
    --pos-outcome-indicator=1 \
    --reference-classes=race_Caucasian,gender_Male

Expected output:

Base model bias metric prior to mitigation strategy

| model   | protected_or_reference   | class                |   num_samples |   equal_odds_difference |   equal_odds_ratio |   demographic_parity_difference |   demographic_parity_ratio |
|---------|--------------------------|----------------------|---------------|-------------------------|--------------------|---------------------------------|----------------------------|
| base    | protected                | race_AfricanAmerican |          3817 |                   0.038 |              0.856 |                           0.033 |                      0.912 |
| base    | protected                | race_Asian           |           142 |                   0.127 |              0.716 |                           0.112 |                      0.674 |
| base    | protected                | race_Hispanic        |           401 |                   0.014 |              0.941 |                           0.017 |                      0.95  |
| base    | protected                | gender_Female        |         10962 |                   0.026 |              0.946 |                           0.01  |                      0.972 |
| base    | reference                | race_Caucasian       |         15278 |                   0.062 |              0.882 |                           0.023 |                      0.937 |
| base    | reference                | gender_Male          |          9392 |                   0.023 |              0.952 |                           0.007 |                      0.98  |



Bias-optimized model metrics

| model   | protected_or_reference   | class                |   num_samples |   equal_odds_difference |   equal_odds_ratio |   demographic_parity_difference |   demographic_parity_ratio |
|---------|--------------------------|----------------------|---------------|-------------------------|--------------------|---------------------------------|----------------------------|
| fair    | protected                | race_AfricanAmerican |          3817 |                   0.025 |              0.944 |                           0.016 |                      0.971 |
| fair    | protected                | race_Asian           |           142 |                   0.132 |              0.798 |                           0.07  |                      0.867 |
| fair    | protected                | race_Hispanic        |           401 |                   0.061 |              0.873 |                           0.001 |                      0.999 |
| fair    | protected                | gender_Female        |         10962 |                   0.028 |              0.957 |                           0.01  |                      0.982 |
| fair    | reference                | race_Caucasian       |         15278 |                   0.017 |              0.974 |                           0.001 |                      0.999 |
| fair    | reference                | gender_Male          |          9392 |                   0.027 |              0.96  |                           0.008 |                      0.986 |

Background and Design

The measure_disparity.py file uses the fairlearn library's built-in functions to derive the equalized_odds_difference, equalized_odds_ratio, demographic_parity_difference, and demographic_parity_ratio values for the input data.

The mitigate_disparity.py file creates a binary classifier that dynamically adjusts weights to minimize the equal odds difference between the protected and reference classes. It also feeds the results into the measure_disparity.py script to measure the reduction in bias after training.

License

Distributed under the BSD 3 License. See license.txt for more information.

Contact

• Ricky Sahu
• Eric Marriott
• Ethan Siegel

Project Link: https://github.com/genhealth/genhealth.github.io