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FAT Forensics: Algorithmic Fairness, Accountability and Transparency Toolbox

FAT Forensics (fatf) is a Python toolbox for evaluating fairness, accountability and transparency of predictive systems. It is built on top of SciPy and NumPy, and is distributed under the 3-Clause BSD license (new BSD).

FAT Forensics implements the state of the art fairness, accountability and transparency (FAT) algorithms for the three main components of any data modelling pipeline: data (raw data and features), predictive models and model predictions. We envisage two main use cases for the package, each supported by distinct features implemented to support it: an interactive research mode aimed at researchers who may want to use it for an exploratory analysis and a deployment mode aimed at practitioners who may want to use it for monitoring FAT aspects of a predictive system.

Please visit the project's web site for more details.



FAT Forensics requires Python 3.5 or higher and the following dependencies:

Package Version
NumPy >=1.10.0
SciPy >=0.13.3

In addition, some of the modules require optional dependencies:

fatf module Package Version
fatf.transparency.predictions.surrogate_explainers scikit-learn >=0.19.2
fatf.transparency.predictions.surrogate_image_explainers scikit-image >=0.17.0
fatf.transparency.predictions.surrogate_image_explainers Pillow >=8.4.0
fatf.vis matplotlib >=3.0.0

User Installation

The easies way to install FAT Forensics is via pip:

pip install fat-forensics

which will only installed the required dependencies. If you want to install the package together with all the auxiliary dependencies please consider using the [all] option:

pip install fat-forensics[all]

The documentation provides more detailed installation instructions.


See the changelog for a development history and project milestones.


We welcome new contributors of all experience levels. The Development Guide has detailed information about contributing code, documentation, tests and more. Some basic development instructions are included below.

Important Links

Source Code

You can check out the latest FAT Forensics source code via git with the command:

git clone


To learn more about contributing to FAT Forensics, please see our Contributing Guide.


You can launch the test suite from the root directory of this repository with:

make test-with-code-coverage

To run the tests you will need to have version 3.9.1 of pytest installed. This package, together with other development dependencies, can be also installed with:

pip install -r requirements-dev.txt

or with:

pip install fat-forensics[dev]

See the Testing section of the Development Guide page for more information.

Please note that the make test-with-code-coverage command will test the version of the package in the local fatf directory and not the one installed since the pytest command is preceded by PYTHONPATH=./. If you want to test the installed version, consider using the command from the Makefile without the PYTHONPATH variable.

To control the randomness during the tests the Makefile sets the random seed to 42 by preceding each test command with FATF_SEED=42, which sets the environment variable responsible for that. More information about the setup of the Testing Environment is available on the development web page in the documentation.

Submitting a Pull Request

Before opening a Pull Request, please have a look at the Contributing page to make sure that your code complies with our guidelines.

Help and Support

For help please have a look at our documentation web page, especially the Getting Started page.


You can reach out to us at:

  • our gitter channel for code-related development discussion; and
  • our mailing list for discussion about the project's future and the direction of the development.

More information about the communication can be found in our documentation on the main page and on the develop page.


If you use FAT Forensics in a scientific publication, we would appreciate citations! Information on how to cite use is available on the citation web page in our documentation.


This project is the result of a collaborative research agreement between Thales and the University of Bristol with the initial funding provided by Thales.