{"id":10609307,"url":"https://github.com/aws/amazon-sagemaker-clarify","last_synced_at":"2026-04-08T14:31:25.481Z","repository":{"id":37633209,"uuid":"257690872","full_name":"aws/amazon-sagemaker-clarify","owner":"aws","description":"Fairness Aware Machine Learning. 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A facet can have value(s) that designates that sample as \"***sensitive***\".\n\n### Label\nThe label is a column or feature which is the target for training a machine learning model. The label can have value(s) that designates that sample as having a \"***positive***\" outcome.\n\n### Bias measure\nA bias measure is a function that returns a bias metric.\n\n### Bias metric\nA bias metric is a numerical value indicating the level of bias detected as determined by a particular bias measure.\n\n### Bias report\nA collection of bias metrics for a given dataset or a combination of a dataset and model.\n\n# Development\n\nIt's recommended that you setup a virtualenv.\n\n```\nvirtualenv -p(which python3) venv\nsource venv/bin/activate.fish\npip install -e .[test]\ncd src/\n../devtool all\n```\n\nFor running unit tests, do `pytest --pspec`. If you are using PyCharm, and cannot see the green run button next to the tests, open `Preferences` -\u003e `Tools` -\u003e `Python Integrated tools`, and set default test runner to `pytest`.\n\nFor Internal contributors, run ```../devtool integ_tests``` after creating virtualenv with the above steps to run the integration tests.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faws%2Famazon-sagemaker-clarify","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faws%2Famazon-sagemaker-clarify","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faws%2Famazon-sagemaker-clarify/lists"}