We present Aequitas, a directed fairness testing framework machine learning models. See the paper Automated Directed Fairness Testing for more details.

• 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.

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 and Scikit-Learn classifiers trained on the same dataset.

The config 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.

python <filename>

eg. python Aequitas_Fully_Directed.py

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