GitHub Support CommunityModel interpretability

Toolkit for Auditing and Mitigating Bias and Fairness of Machine Learning Systems.

Responsibly is developed for practitioners and researchers in mind, but also for learners. Therefore, it is compatible with data science and machine learning tools of trade in Python, such as Numpy, Pandas, and especially scikit-learn.

The primary goal is to be one-shop-stop for auditing bias and fairness of machine learning systems, and the secondary one is to mitigate bias and adjust fairness through algorithmic interventions. Besides, there is a particular focus on NLP models.


Responsibly consists of three sub-packages:

Collection of common benchmark datasets from fairness research.
Demographic fairness in binary classification, including metrics and algorithmic interventions.
Metrics and debiasing methods for bias (such as gender and race) in word embedding.

Official website

Tutorial and documentation

Enter your contact information to continue reading