fairdo 0.1.10

Fairness-Agnostic Data Optimization

FairDo is a Python package for mitigating bias in data. It can be used to create datasets to comply with the AI Act:

[...] data sets should also have the appropriate statistical properties, including as regards the persons or groups of persons in relation to whom the high-risk AI system is intended to be used, with specific attention to the mitigation of possible biases in the data sets. [...]

• Official repository of: Towards Fairness and Privacy: A Novel Data Pre-processing Optimization Framework for Non-binary Protected Attributes
• Documentation: https://fairdo.readthedocs.io/en/latest/
• Source Code: https://github.com/mkduong-ai/fairdo/tree/main

Why FairDo?

• Fairness: Minimizes discrimination in datasets
• Interpretability and integrity: Under- and oversampling technique
• Works with tabular data: pandas.DataFrame
• Simplicity: Follows .fit_transform() convention and includes many examples
• Handles a variety of cases: non-binary groups, multiple protected attributes, individual fairness
• Customizable: custom fairness definition, solver, objective

How does it work?

The pre-processing methods (fairdo.preprocessing.HeuristicWrapper and fairdo.preprocessing.DefaultPreprocessing) work by removing discriminatory data points. By doing so, the dataset becomes much more balanced and less biased towards a particular social group. We approach this task as a combinatorial optimization problem, which means selecting a subset of the dataset that minimizes the discrimination score. Because there are exponentially many subsets, our approach uses genetic algorithms.

Quick Example

In the following example, we use the COMPAS [1] dataset. The protected attribute is race and the label is recidivism. Here, we deploy the default pre-processor, which internally uses a genetic algorithm, to remove discriminatory samples of the given dataset. The default pre-processor prevents removing all individuals of a single group.

# fairdo package
from fairdo.utils.dataset import load_data
from fairdo.preprocessing import DefaultPreprocessing
# fairdo metrics
from fairdo.metrics import statistical_parity_abs_diff_max

# Loading a sample dataset with all required information
# data is a pandas.DataFrame
data, label, protected_attributes = load_data('compas', print_info=False)

# Initialize DefaultPreprocessing object
preprocessor = DefaultPreprocessing(protected_attribute=protected_attributes[0],
                                    label=label)

# Fit and transform the data
data_fair = preprocessor.fit_transform(dataset=data)

# Print no. samples and discrimination before and after
disc_before = statistical_parity_abs_diff_max(data[label],
                                              data[protected_attributes[0]].to_numpy())
disc_after = statistical_parity_abs_diff_max(data_fair[label],
                                             data_fair[protected_attributes[0]].to_numpy())
print(len(data), disc_before)
print(len(data_fair), disc_after)

By running this example, the resulting dataset usually has a statistical disparity score of <1% (max. score between all five races), while the original dataset exhibits 27% statistical disparity.

Advices

:rocket: For a quick start, use the DefaultPreprocessing class with the default settings. An example is given in tutorials/1. Default Preprocessor.

:white_check_mark: For data quality, you want to keep the original data $D$ and only add fair synthetic data $G$ on top of it. (We include examples in tutorials/ where we use the SDV package to generate synthetic data.) For this, you need to specify .fit_transform(approach='add') for the pre-processors fairdo.preprocessing.HeuristicWrapper and fairdo.preprocessing.DefaultPreprocessing. This will only add the fair pre-processed synthetic data to the original data.

:dash: When having limited data, we advise employing synthetic data $G$ additionally and merge it with the original data $D$, i.e., $D \cup G$. The pre-processor can then be used on the merged data $D \cup G$ to ensure fairness. It is also possible to use the methodology for data quality, as described above.

:briefcase: When anonymity is required, only use synthetic data $G$ and do not merge it with the original data $D$. The generated data $G$ can then be pre-processed with our methods to ensure fairness.

Installation

First, setup a Python environment. We recommend using Miniconda. Activate the created environment afterwards and finally install our package. A detailed guide is given as follows.

Dependencies

Python (==3.8), numpy, scipy, pandas, sklearn

Setup Conda Environment

Download Miniconda here.

# Create a conda virtual environment
conda create -n "venv" python=3.8

# Activate conda environment
conda activate venv

OR

Setup Python Environment

# Create a virtual environment
python -m venv .venv

# Activate the virtual environment
# On Windows:
.venv\Scripts\activate

# On macOS and Linux:
source .venv/bin/activate

PyPI Distribution (recommended)

The package is distributed via PyPI and can be directly installed with:

pip install fairdo

Manual Installation (latest version)

To install the latest (development) version, execute following commands:

# Clone repo
git clone https://github.com/mkduong-ai/fairdo.git

# Move to repo folder
cd fairdo

# Install from source
python setup.py install

Install Optional Dependencies

To use the synthetic data generation, you can install the SDV package by executing the following command:

pip install sdv==1.10.0

We did not include the SDV package as a dependency, because it is not required for the core functionality of the FairDo package. Using any other synthetic data generation package is also possible. Still, some examples in the tutorials/ folder require the SDV package.

Citation

When using FairDo in your work, cite our paper:

@inproceedings{duong2023framework,
  title={Towards Fairness and Privacy: A Novel Data Pre-processing Optimization Framework for Non-binary Protected Attributes},
  author={Duong, Manh Khoi and Conrad, Stefan},
  booktitle={Data Science and Machine Learning},
  publisher={Springer Nature Singapore},
  number={CCIS 1943},
  series={AusDM: Australasian Conference on Data Science and Machine Learning},
  year={2023},
  pages={105--120},
  isbn={978-981-99-8696-5},
}

Notes

We credit OpenMoji for the emojis used in our logo.