The AI Fairness 360 toolkit is an extensible open-source library containing techniques developed by the research community to help detect and mitigate bias in machine learning models throughout the AI application lifecycle. AI Fairness 360 package is available in both Python and R.

The AI Fairness 360 package includes

1. a comprehensive set of metrics for datasets and models to test for biases,
2. explanations for these metrics, and
3. algorithms to mitigate bias in datasets and models. It is designed to translate algorithmic research from the lab into the actual practice of domains as wide-ranging as finance, human capital management, healthcare, and education. We invite you to use it and improve it.

The AI Fairness 360 interactive experience provides a gentle introduction to the concepts and capabilities. The tutorials and other notebooks offer a deeper, data scientist-oriented introduction. The complete API is also available.

Being a comprehensive set of capabilities, it may be confusing to figure out which metrics and algorithms are most appropriate for a given use case. To help, we have created some guidance material that can be consulted.

We have developed the package with extensibility in mind. This library is still in development. We encourage the contribution of your metrics, explainers, and debiasing algorithms.

Get in touch with us on Slack (invitation here)!

• Optimized Preprocessing (Calmon et al., 2017)
• Disparate Impact Remover (Feldman et al., 2015)
• Equalized Odds Postprocessing (Hardt et al., 2016)
• Reweighing (Kamiran and Calders, 2012)
• Reject Option Classification (Kamiran et al., 2012)
• Prejudice Remover Regularizer (Kamishima et al., 2012)
• Calibrated Equalized Odds Postprocessing (Pleiss et al., 2017)
• Learning Fair Representations (Zemel et al., 2013)
• Adversarial Debiasing (Zhang et al., 2018)
• Meta-Algorithm for Fair Classification (Celis et al., 2018)
• Rich Subgroup Fairness (Kearns, Neel, Roth, Wu, 2018)
• Exponentiated Gradient Reduction (Agarwal et al., 2018)
• Grid Search Reduction (Agarwal et al., 2018, Agarwal et al., 2019)
• Fair Data Adaptation (Plečko and Meinshausen, 2020, Plečko et al., 2021)
• Sensitive Set Invariance/Sensitive Subspace Robustness (Yurochkin and Sun, 2020, Yurochkin et al., 2019)

• Comprehensive set of group fairness metrics derived from selection rates and error rates including rich subgroup fairness
• Comprehensive set of sample distortion metrics
• Generalized Entropy Index (Speicher et al., 2018)
• Differential Fairness and Bias Amplification (Foulds et al., 2018)
• Bias Scan with Multi-Dimensional Subset Scan (Zhang, Neill, 2017)

install.packages("aif360")

For more details regarding the R setup, please refer to instructions here.

Supported Python Configurations:

AIF360 requires specific versions of many Python packages which may conflict with other projects on your system. A virtual environment manager is strongly recommended to ensure dependencies may be installed safely. If you have trouble installing AIF360, try this first.

Conda is recommended for all configurations though Virtualenv is generally interchangeable for our purposes. Miniconda is sufficient (see the difference between Anaconda and Miniconda if you are curious) if you do not already have conda installed.

Then, to create a new Python 3.11 environment, run:

conda create --name aif360 python=3.11
conda activate aif360

The shell should now look like (aif360) $. To deactivate the environment, run:

(aif360)$ conda deactivate

The prompt will return to $ .

To install the latest stable version from PyPI, run:

pip install aif360

Note: Some algorithms require additional dependencies (although the metrics will all work out-of-the-box). To install with certain algorithm dependencies included, run, e.g.:

pip install 'aif360[LFR,OptimPreproc]'

or, for complete functionality, run:

pip install 'aif360[all]'

The options for available extras are: OptimPreproc, LFR, AdversarialDebiasing, DisparateImpactRemover, LIME, ART, Reductions, FairAdapt, inFairness, LawSchoolGPA, notebooks, tests, docs, all

If you encounter any errors, try the Troubleshooting steps.

Clone the latest version of this repository:

git clone https://github.com/Trusted-AI/AIF360

If you'd like to run the examples, download the datasets now and place them in their respective folders as described in aif360/data/README.md.

Then, navigate to the root directory of the project and run:

pip install --editable '.[all]'

To run the example notebooks, complete the manual installation steps above. Then, if you did not use the [all] option, install the additional requirements as follows:

pip install -e '.[notebooks]'

Finally, if you did not already, download the datasets as described in aif360/data/README.md.

If you encounter any errors during the installation process, look for your issue here and try the solutions.

See the Install TensorFlow with pip page for detailed instructions.

Note: we require 'tensorflow >= 1.13.1'.

Once tensorflow is installed, try re-running:

pip install 'aif360[AdversarialDebiasing]'

TensorFlow is only required for use with the aif360.algorithms.inprocessing.AdversarialDebiasing class.

On MacOS, you may first have to install the Xcode Command Line Tools if you never have previously:

xcode-select --install

On Windows, you may need to download the Microsoft C++ Build Tools for Visual Studio 2019. See the CVXPY Install page for up-to-date instructions.

Then, try reinstalling via:

pip install 'aif360[OptimPreproc]'

CVXPY is only required for use with the aif360.algorithms.preprocessing.OptimPreproc class.

The examples directory contains a diverse collection of jupyter notebooks that use AI Fairness 360 in various ways. Both tutorials and demos illustrate working code using AIF360. Tutorials provide additional discussion that walks the user through the various steps of the notebook. See the details about tutorials and demos here

A technical description of AI Fairness 360 is available in this paper. Below is the bibtex entry for this paper.

@misc{aif360-oct-2018,
    title = "{AI Fairness} 360:  An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias",
    author = {Rachel K. E. Bellamy and Kuntal Dey and Michael Hind and
	Samuel C. Hoffman and Stephanie Houde and Kalapriya Kannan and
	Pranay Lohia and Jacquelyn Martino and Sameep Mehta and
	Aleksandra Mojsilovic and Seema Nagar and Karthikeyan Natesan Ramamurthy and
	John Richards and Diptikalyan Saha and Prasanna Sattigeri and
	Moninder Singh and Kush R. Varshney and Yunfeng Zhang},
    month = oct,
    year = {2018},
    url = {https://arxiv.org/abs/1810.01943}
}

• Introductory video to AI Fairness 360 by Kush Varshney, September 20, 2018 (32 mins)

The development fork for Rich Subgroup Fairness (inprocessing/gerryfair_classifier.py) is here. Contributions are welcome and a list of potential contributions from the authors can be found here.