Fairness Aware Machine Learning. Bias detection and mitigation for datasets and models.
Amazon Sagemaker Clarify
Bias detection and mitigation for datasets and models.
To install the package from PIP you can simply do:
pip install smclarify
You can see examples on running the Bias metrics on the notebooks in the examples folder.
A facet is column or feature that will be used to measure bias against. A facet can have value(s) that designates that sample as "sensitive".
The label is a column or feature which is the target for training a machine learning model. The label can have value(s) that designates that sample as having a "positive" outcome.
A bias measure is a function that returns a bias metric.
A bias metric is a numerical value indicating the level of bias detected as determined by a particular bias measure.
A collection of bias metrics for a given dataset or a combination of a dataset and model.
It's recommended that you setup a virtualenv.
virtualenv -p(which python3) venv
source venv/bin/activate.fish
pip install -e .[test]
cd src/
../devtool all
For running unit tests, do pytest --pspec
. If you are using PyCharm, and cannot see the green run button next to the tests, open Preferences
-> Tools
-> Python Integrated tools
, and set default test runner to pytest
.
For Internal contributors, run ../devtool integ_tests
after creating virtualenv with the above steps to run the integration tests.