Responsibly
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About
Responsibly is a toolkit for auditing and mitigating bias and fairness in machine learning systems, with a focus on NLP models. It is designed for practitioners, researchers, and learners and is compatible with Python data science and machine learning tools like Numpy, Pandas, and scikit-learn. It consists of three sub-packages: responsibly.dataset (benchmark datasets), responsibly.fairness (demographic fairness in binary classification), and responsibly.we (metrics and debiasing methods for word embeddings). Responsibly's fairness functionality aligns with the book 'Fairness and Machine Learning - Limitations and Opportunities'.
Platform
Task
Features
• debiasing methods for word embeddings
• benchmark datasets
• fairness metrics
• algorithmic interventions
• mitigating bias
• auditing bias
Pricing Plans
Free
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