https://github.com/aws/amazon-sagemaker-clarify

Fairness Aware Machine Learning. Bias detection and mitigation for datasets and models.
https://github.com/aws/amazon-sagemaker-clarify

fairness fairness-ai fairness-ml machine-learning

Last synced: 4 days ago
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Fairness Aware Machine Learning. Bias detection and mitigation for datasets and models.

• Host: GitHub
• URL: https://github.com/aws/amazon-sagemaker-clarify
• Owner: aws
• License: apache-2.0
• Created: 2020-04-21T19:08:09.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2024-03-25T22:15:13.000Z (10 months ago)
• Last Synced: 2025-01-15T12:59:31.912Z (11 days ago)
• Topics: fairness, fairness-ai, fairness-ml, machine-learning
• Language: Python
• Homepage:
• Size: 620 KB
• Stars: 68
• Watchers: 20
• Forks: 38
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md
  • Codeowners: CODEOWNERS

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• AwesomeResponsibleAI - smclafify

README

        
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# smclarify

Amazon Sagemaker Clarify

Bias detection and mitigation for datasets and models.

# Installation

To install the package from PIP you can simply do:

```

pip install smclarify

```

You can see examples on running the Bias metrics on the notebooks in the [examples folder](https://github.com/aws/amazon-sagemaker-clarify/tree/master/examples).

# Terminology

### Facet

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***".

### Label

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.

### Bias measure

A bias measure is a function that returns a bias metric.

### Bias metric

A bias metric is a numerical value indicating the level of bias detected as determined by a particular bias measure.

### Bias report

A collection of bias metrics for a given dataset or a combination of a dataset and model.

# Development

It's recommended that you setup a virtualenv.

```

virtualenv -p(which python3) venv

source venv/bin/activate.fish

pip install -e .[test]

cd src/

../devtool all

```

For running unit tests, do `pytest --pspec`. If you are using PyCharm, and cannot see the green run button next to the tests, open `Preferences` -> `Tools` -> `Python Integrated tools`, and set default test runner to `pytest`.

For Internal contributors, run ```../devtool integ_tests``` after creating virtualenv with the above steps to run the integration tests.