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.
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References in zbMATH (referenced in 1 article , 1 standard article )
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- Kacper Sokol; Alexander Hepburn; Rafael Poyiadzi; Matthew Clifford; Raul Santos-Rodriguez; Peter Flach: FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems (2020) not zbMATH