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

arXiv: https://arxiv.org/abs/1909.05167 · HTML: https://mlsanity.com/paper/1909.05167/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2019.

## Built on by (most cited first, 11 of 11)

- [Fair Preprocessing: Towards Understanding Compositional Fairness of Data Transformers in Machine Learning Pipeline](https://mlsanity.com/paper/2106.06054/) (2021)
- [De-biasing "bias" measurement](https://mlsanity.com/paper/2205.05770/) (2022)
- [OxonFair: A Flexible Toolkit for Algorithmic Fairness](https://mlsanity.com/paper/2407.13710/) (2024)
- [Auditing of AI: Legal, Ethical and Technical Approaches](https://mlsanity.com/paper/2407.06235/) (2024)
- [Predicting brain-age from raw T 1 -weighted Magnetic Resonance Imaging data using 3D Convolutional Neural Networks](https://mlsanity.com/paper/2103.11695/) (2021)
- [FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems](https://mlsanity.com/paper/2209.03805/) (2022)
- [What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components](https://mlsanity.com/paper/2209.03813/) (2022)
- [Identification, explanation and clinical evaluation of hospital patient subtypes](https://mlsanity.com/paper/2301.08019/) (2023)
- [Hypothesis Testing for Class-Conditional Label Noise](https://mlsanity.com/paper/2103.02630/) (2021)
- [Applications and Challenges of Fairness APIs in Machine Learning Software](https://mlsanity.com/paper/2508.16377/) (2025)
- [humancompatible.detect: a Python Toolkit for Detecting Bias in AI Models](https://mlsanity.com/paper/2509.24340/) (2025)

## Abstract (arXiv)

Today, artificial intelligence systems driven by machine learning algorithms can be in a position to take important, and sometimes legally binding, decisions about our everyday lives. In many cases, however, these systems and their actions are neither regulated nor certified. To help counter the potential harm that such algorithms can cause we developed an open source toolbox that can analyse selected fairness, accountability and transparency aspects of the machine learning process: data (and their features), models and predictions, allowing to automatically and objectively report them to relevant stakeholders. In this paper we describe the design, scope, usage and impact of this Python package, which is published under the 3-Clause BSD open source licence.