https://github.com/sakshiudeshi/Aequitas

Aequitas, a directed fairness testing framework machine learning models.
https://github.com/sakshiudeshi/Aequitas

machine-learning software-engineering software-testing

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Aequitas, a directed fairness testing framework machine learning models.

• Host: GitHub
• URL: https://github.com/sakshiudeshi/Aequitas
• Owner: sakshiudeshi
• Created: 2018-07-10T12:56:50.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2022-02-18T04:07:40.000Z (almost 3 years ago)
• Last Synced: 2024-08-05T21:28:19.536Z (5 months ago)
• Topics: machine-learning, software-engineering, software-testing
• Language: Python
• Homepage:
• Size: 2.46 MB
• Stars: 9
• Watchers: 5
• Forks: 10
• Open Issues: 4
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-algofairness - Aequitas

README

        
# Aequitas

We present Aequitas, a directed fairness testing framework machine learning models. See the paper [Automated Directed Fairness Testing](https://arxiv.org/abs/1807.00468) for more details.

## Prerequisites

* Python 2.7.15

* numpy 1.14.5

* scipy 1.1.0

* scikit-learn 1.19.0

The authors used Pycharm CE 2017.2.3 as the development IDE.

## Background

There are 3 test generation strategies in our suite, namely Aequitas Random, Aequitas Semi-Directed and Aequitas Fully Directed. There are files to evaluate [Fair SVM](https://github.com/mbilalzafar/fair-classification) and Scikit-Learn classifiers trained on the same [dataset](http://archive.ics.uci.edu/ml/datasets/Adult).

## Config

The [config](config.py) file has the following data:

* params : The number of parameters in the data

* sensitive_param: The parameter under test.

* input_bounds: The bounds of each parameter

* classifier_name: Pickled scikit-learn classifier under test (only applicable to the sklearn files)

* threshold: Discrimination threshold.

* perturbation_unit: By what unit would the user like to perturb the input in the local search.

* retraining_inputs: Inputs to be used for the retraining. Please see [this file](Retrain_Example_File.txt).

## Demo

`python `

eg. `python Aequitas_Fully_Directed.py`

## Contact

* Please contact [email protected] for any comments/questions

## Citation 

If you use any part of this code, please cite the following paper

```

@inproceedings{aequitas,

  title={Automated directed fairness testing},

  author={Udeshi, Sakshi and Arora, Pryanshu and Chattopadhyay, Sudipta},

  booktitle={Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering},

  pages={98--108},

  year={2018}

}

```