  Predictive systems, in particular machine learning algorithms, can take
important, and sometimes legally binding, decisions 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, their
qualities such as fairness, accountability and transparency (FAT) are of
paramount importance. To ensure high-quality, fair, transparent and reliable
predictive systems, we developed an open source Python package called FAT
Forensics. It can inspect important fairness, accountability and transparency
aspects of predictive algorithms to automatically and objectively report them
back to engineers and users of such systems. Our toolbox can evaluate all
elements of a predictive pipeline: data (and their features), models and
predictions. Published under the BSD 3-Clause open source licence, FAT
Forensics is opened up for personal and commercial usage.
