FAT Forensics: A Python Toolbox for Algorithmic Fairness, Accountability and Transparency

Machine learning algorithms can take important decisions, sometimes legally binding, about our everyday life. In most cases, however, these systems and decisions are neither regulated nor certified. Given the potential harm that these algorithms can cause, qualities such as fairness, accountability and transparency of predictive systems are of paramount importance. Recent literature suggested voluntary self-reporting on these aspects of predictive systems -- e.g., data sheets for data sets -- but their scope is often limited to a single component of a machine learning pipeline, and producing them requires manual labour. To resolve this impasse and ensure high-quality, fair, transparent and reliable machine learning systems, we developed an open source toolbox that can inspect selected fairness, accountability and transparency aspects of these systems to automatically and objectively report them back to their engineers and users. We describe design, scope and usage examples of this Python toolbox in this paper. The toolbox provides functionality for inspecting fairness, accountability and transparency of all aspects of the machine learning process: data (and their features), models and predictions. It is available to the public under the BSD 3-Clause open source licence.