Responsibly
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About
Responsibly is a toolkit designed for auditing and mitigating bias and fairness in machine learning systems, aiming to assist practitioners, researchers, and learners. Compatible with Python data science tools like Numpy, Pandas, and scikit-learn, it provides functionalities aligned with fairness research. Responsibly consists of three sub-packages: responsibly.dataset for benchmark datasets, responsibly.fairness focusing on demographic fairness in binary classification metrics and interventions, and responsibly.we for addressing bias in word embeddings. The aim is to serve as a comprehensive solution for navigating bias and fairness issues in machine learning models, with features including algorithm auditing and intervention adjustments.
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Task
Features
• auditing of machine learning systems
• useful for researchers and practitioners
• focus on algorithms and nlp models
• data and metrics for demographic fairness
• integration with popular python libraries
• mitigating bias and improving fairness
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