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Predictive systems, in particular machine learning algorithms, can takeimportant, and sometimes legally binding, decisions about our everyday life. Inmost cases, however, these systems and decisions are neither regulated norcertified. Given the potential harm that these algorithms can cause, theirqualities such as fairness, accountability and transparency (FAT) are ofparamount importance. To ensure high-quality, fair, transparent and reliablepredictive systems, we developed an open source Python package called FATForensics. It can inspect important fairness, accountability and transparencyaspects of predictive algorithms to automatically and objectively report themback to engineers and users of such systems. Our toolbox can evaluate allelements of a predictive pipeline: data (and their features), models andpredictions. Published under the BSD 3-Clause open source licence, FATForensics is opened up for personal and commercial usage.

FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems