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The NLP Bias Identification Toolkit

Project description


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(

# see a summary of the detection

# 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:
  • 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

Preview/build the documentation with mkdocs

To preview the documentation run make doc-preview. This will launch a preview of the documentation on 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


Follow the google style guide for python:

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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