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
Task
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
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