The NLP Bias Identification Toolkit
Project description
biaslyze
The NLP Bias Identification Toolkit
Usage example
from biaslyze.bias_detectors import CounterfactualBiasDetector
bias_detector = CounterfactualBiasDetector()
# detect bias in the model based on the given texts
# here, clf is a scikit-learn text classification pipeline trained for a binary classification task
detection_res = bias_detector.process(
texts=texts,
predict_func=clf.predict_proba
)
# see a summary of the detection
detection_res.report()
# visualize the counterfactual scores
detection_res.visualize_counterfactual_scores(concept="religion", top_n=10)
Example output:
Development setup
- First you need to install poetry to manage your python environment: https://python-poetry.org/docs/#installation
- Run
make install
to install the dependencies and get the spacy basemodels. - Now you can use
biaslyze
in your jupyter notebooks.
Adding concepts and keywords
You can add concepts and new keywords for existing concepts by editing concepts.py.
Preview/build the documentation with mkdocs
To preview the documentation run make doc-preview
. This will launch a preview of the documentation on http://127.0.0.1:8000/
.
To build the documentation html run make doc
.
Run the automated tests
make test
Style guide
We are using isort and black: make style
For linting we are running ruff: make lint
Contributing
Follow the google style guide for python: https://google.github.io/styleguide/pyguide.html
This project uses black, isort and ruff to enforce style. Apply it by running make style
and make lint
.
Project details
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