Fairness Aware Machine Learning. Bias detection and mitigation for datasets and models. A facet is column or feature that will be used to measure bias against. A facet can have value(s) that designates that sample as "sensitive". Bias detection and mitigation for datasets and models. The 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. A bias measure is a function that returns a bias metric. A bias metric is a numerical value indicating the level of bias detected as determined by a particular bias measure. A collection of bias metrics for a given dataset or a combination of a dataset and model.

Features

  • Install the package from PIP
  • Bias measure
  • Bias report
  • Bias metric
  • You can see examples on running the Bias metrics on the notebooks in the examples folder
  • Mitigation for datasets and models

Project Samples

Project Activity

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Categories

UML, Machine Learning

License

Apache License V2.0

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Additional Project Details

Programming Language

Python

Related Categories

Python UML Tool, Python Machine Learning Software

Registered

2022-06-29