Promoting Fairness through Hyperparameter Optimization

This repository contains ML artifacts and other materials from the experiments performed on the paper.

Key Contributions

• An approach for promoting model fairness that can be easily plugged into current ML pipelines with no extra development or computational cost.
• A set of competitive fairness-aware HO algorithms for multi-objective optimization of the fairness-accuracy trade-off that are agnostic to both the explored hyperparameter space and the objective metrics.
• Strong empirical evidence that hyperparameter optimization (HO) is an effective way to navigate the fairness-accuracy trade-off.
• A heuristic to automatically set the fairness-accuracy trade-off parameter.
• Competitive results on a real-world fraud detection use case, as well as on three datasets from the fairness literature (Adult, COMPAS, Donors Choose).

Repository Structure

• data contains detailed artifacts generated from each experiment;
  • all_tuner_iters_evals_<dataset>.csv.gz contains all HO iterations from all tuners for each dataset;
  • <dataset>_non-aggregated-results.csv contains one row per each HO run, for all tuners except TPE and FairTPE;
  • all-datasets-with-TPE-tuner_non-aggregated-results.csv contains one row per each HO run for TPE and FairTPE (all datasets on the same file);
  • results_all_datasets.csv contains one row per each HO run for all tuners, for all datasets;
  • AOF-EG-experiment_non-aggregated-results.csv contains data from the EG experiment (adding the Exponentiated Gradient reduction bias-reduction method to the search space);
• code contains misc. jupyter notebooks used for the paper;
  • code/plots.ipynb generates plots for all datasets from the provided data files;
  • code/stats.ipynb computes validation/test results for each experiment, as well as p-values of statistical difference between hyperparameter tuners;
• imgs contains all generated plots for all datasets (all plots from the paper plus a few that didn’t make it due to space);
• hyperparameters contains details on the hyperparameter search space used for all HO tasks;

Fairband: Selected Fairness-Accuracy Trade-off, discriminated by Model Type

• Plot for the EG experiment on the Adult dataset here.
• Experiment: running Fairband (15 runs) on the AOF and Adult datasets, supplied with the following model choices: Neural Network (NN), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), LightGBM (LGBM), and Exponentiated Gradient reduction for fair classification (EG).
• EG is a state-of-the-art bias reduction method available at fairlearn.
• As shown by the plot, blindly applying bias reduction techniques may lead to suboptimal fairness-accuracy trade-offs. In this example, EG is dominated by LGBM models on the AOF dataset, and by NN models on the Adult dataset. Fairband should be used in conjunction with a wide portfolio of model choices to achieve fairness.

Citing

@inproceedings{cruz2021promoting,
    title={Promoting Fairness through Hyperparameter Optimization},
    author={Cruz, Andr{\'{e}} F. and Saleiro, Pedro and Bel{\'{e}}m, Catarina and Soares, Carlos and Bizarro, Pedro},
    booktitle={2021 {IEEE} International Conference on Data Mining ({ICDM})},   
    year={2021},
    pages={1036-1041},
    publisher=,
    url={https://doi.org/10.1109/ICDM51629.2021.00119},
    doi={10.1109/ICDM51629.2021.00119}
}