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AI Tech Suite
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Responsibly

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About

Responsibly is a toolkit designed for auditing and mitigating bias and fairness in machine learning systems, catering to practitioners, researchers, and learners in data science. It is compatible with Python libraries like Numpy, Pandas, and scikit-learn. The tool focuses on providing a one-stop-shop for addressing bias and fairness, primarily through algorithmic interventions in NLP models. Responsibly contains three sub-packages for dataset collection, demographic fairness assessment, and word embedding bias metrics and debiasing methods. The tool aligns with the principles outlined in the book 'Fairness and Machine Learning' by Solon Barocas, Moritz Hardt, and Arvind Narayanan.

Platform
Web
Keywords
machine learningnlpfairnessbiasauditing
Task
bias auditing
Features

• word embedding bias analysis

• collection of benchmark datasets

• support for multiple python libraries

• demographic fairness assessments

• fairness metrics

• mitigating algorithmic bias

• auditing machine learning systems

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