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.