biaslyze 0.0.4a0

The NLP Bias Identification Toolkit

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.