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Responsibly

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About

Responsibly is a toolkit developed for practitioners, researchers, and learners for auditing bias and fairness of machine learning systems. It focuses on algorithmic interventions. It includes sub-packages for datasets, fairness in binary classification, and bias in word embedding. It is compatible with Python data science tools such as Numpy, Pandas, and scikit-learn. It provides metrics and debiasing methods for bias in word embeddings, including gender and race.

Platform
Web
Keywords
machine learningnlpfairnessauditbias
Task
bias mitigation

Features

metrics and debiasing methods for bias (such as gender and race) in word embedding.

demographic fairness in binary classification, including metrics and algorithmic interventions.

collection of common benchmark datasets from fairness research.

toolkit for auditing and mitigating bias and fairness of machine learning systems

Pricing Plans

Free
Free Plan

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