Metadata-Version: 2.1 Name: aif360 Version: 0.5.0 Summary: IBM AI Fairness 360 Home-page: https://github.com/Trusted-AI/AIF360 Author: aif360 developers Author-email: aif360@us.ibm.com License: Apache License 2.0 Requires-Python: >=3.7 Description-Content-Type: text/markdown License-File: LICENSE Requires-Dist: numpy (>=1.16) Requires-Dist: scipy (>=1.2.0) Requires-Dist: pandas (>=0.24.0) Requires-Dist: scikit-learn (>=1.0) Requires-Dist: matplotlib Provides-Extra: art Requires-Dist: adversarial-robustness-toolbox (>=1.0.0) ; extra == 'art' Provides-Extra: adversarialdebiasing Requires-Dist: tensorflow (>=1.13.1) ; extra == 'adversarialdebiasing' Provides-Extra: disparateimpactremover Requires-Dist: BlackBoxAuditing ; extra == 'disparateimpactremover' Provides-Extra: fairadapt Requires-Dist: rpy2 ; extra == 'fairadapt' Provides-Extra: lfr Requires-Dist: torch ; extra == 'lfr' Provides-Extra: lime Requires-Dist: lime ; extra == 'lime' Provides-Extra: lawschoolgpa Requires-Dist: tempeh ; extra == 'lawschoolgpa' Provides-Extra: optimpreproc Requires-Dist: cvxpy (>=1.0) ; extra == 'optimpreproc' Provides-Extra: reductions Requires-Dist: fairlearn (~=0.7) ; extra == 'reductions' Provides-Extra: all Requires-Dist: seaborn ; extra == 'all' Requires-Dist: jinja2 (<3.1.0) ; extra == 'all' Requires-Dist: ipympl ; extra == 'all' Requires-Dist: adversarial-robustness-toolbox (>=1.0.0) ; extra == 'all' Requires-Dist: pytest (>=3.5) ; extra == 'all' Requires-Dist: torch ; extra == 'all' Requires-Dist: BlackBoxAuditing ; extra == 'all' Requires-Dist: tensorflow (>=1.13.1) ; extra == 'all' Requires-Dist: sphinx (<2) ; extra == 'all' Requires-Dist: tempeh ; extra == 'all' Requires-Dist: cvxpy (>=1.0) ; extra == 'all' Requires-Dist: jupyter ; extra == 'all' Requires-Dist: tqdm ; extra == 'all' Requires-Dist: igraph[plotting] ; extra == 'all' Requires-Dist: fairlearn (~=0.7) ; extra == 'all' Requires-Dist: rpy2 ; extra == 'all' Requires-Dist: lime ; extra == 'all' Requires-Dist: lightgbm ; extra == 'all' Requires-Dist: sphinx-rtd-theme ; extra == 'all' Provides-Extra: docs Requires-Dist: sphinx (<2) ; extra == 'docs' Requires-Dist: jinja2 (<3.1.0) ; extra == 'docs' Requires-Dist: sphinx-rtd-theme ; extra == 'docs' Provides-Extra: notebooks Requires-Dist: jupyter ; extra == 'notebooks' Requires-Dist: tqdm ; extra == 'notebooks' Requires-Dist: igraph[plotting] ; extra == 'notebooks' Requires-Dist: lightgbm ; extra == 'notebooks' Requires-Dist: seaborn ; extra == 'notebooks' Requires-Dist: ipympl ; extra == 'notebooks' Provides-Extra: tests Requires-Dist: pytest (>=3.5) ; extra == 'tests' Requires-Dist: cvxpy (>=1.0) ; extra == 'tests' Requires-Dist: tensorflow (>=1.13.1) ; extra == 'tests' Requires-Dist: BlackBoxAuditing ; extra == 'tests' Requires-Dist: torch ; extra == 'tests' Requires-Dist: lime ; extra == 'tests' Requires-Dist: adversarial-robustness-toolbox (>=1.0.0) ; extra == 'tests' Requires-Dist: fairlearn (~=0.7) ; extra == 'tests' Requires-Dist: rpy2 ; extra == 'tests' Requires-Dist: jupyter ; extra == 'tests' Requires-Dist: tqdm ; extra == 'tests' Requires-Dist: igraph[plotting] ; extra == 'tests' Requires-Dist: lightgbm ; extra == 'tests' Requires-Dist: seaborn ; extra == 'tests' Requires-Dist: ipympl ; extra == 'tests' Requires-Dist: tempeh ; extra == 'tests' The AI Fairness 360 toolkit is an open-source library to help detect and mitigate bias in machine learning models. The AI Fairness 360 Python package includes a comprehensive set of metrics for datasets and models to test for biases, explanations for these metrics, and algorithms to mitigate bias in datasets and models. We have developed the package with extensibility in mind. This library is still in development. We encourage the contribution of your datasets, metrics, explainers, and debiasing algorithms.