# Aequitas Flow step-by-step: a Fair ML optimization framework

DevFeed: [Aequitas Flow step-by-step: a Fair ML optimization framework](<https://devfeed.tech/articles/aequitas-flow-step-by-step-a-fair-ml-optimization-framework-26292.md>)

Original publisher: [Read original article](<https://medium.com/feedzaitech/aequitas-flow-step-by-step-a-fair-ml-optimization-framework-9d2f90309ba4?source=rss----e11168e7fe6b---4>)

Author: Sérgio Jesus

Published: 2024-08-12T14:35:43Z

Content type: tutorial

Language: en

Sources: [Feedzai](<https://devfeed.tech/sources/feedzai.md>)

Topics: [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [audit](<https://devfeed.tech/topics/audit.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [pandas](<https://devfeed.tech/topics/pandas.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [interfaces](<https://devfeed.tech/topics/interfaces.md>)

Tags: [audit](<https://devfeed.tech/tags/audit.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [fairness](<https://devfeed.tech/tags/fairness.md>), [interfaces](<https://devfeed.tech/tags/interfaces.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [ml](<https://devfeed.tech/tags/ml.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [pandas](<https://devfeed.tech/tags/pandas.md>), [reproducibility](<https://devfeed.tech/tags/reproducibility.md>), [research](<https://devfeed.tech/tags/research.md>), [responsible-ai](<https://devfeed.tech/tags/responsible-ai.md>)

## AI overview

This tutorial introduces Aequitas Flow, an open-source framework for standardized experiments with fair machine-learning algorithms. It explains how the framework extends Aequitas fairness auditing with reproducible experimentation, support for custom datasets and methods, and workflows for practitioners selecting models and researchers running benchmarks.

## Source excerpt

By Sérgio Jesus, Inês Silva, Pedro Saleiro, Hugo Ferreira, Pedro Bizarro In this blog post we will visit Aequitas Flow, an Open-Source framework designed to run complete and standardized experiments of Fair ML algorithms. We encourage you to try Aequitas Flow with the Google Colab Notebooks, which are available in the project's GitHub repository. This blog post is based on the paper by Sérgio Jesus, Pedro Saleiro, Inês Silva, Beatriz M. Jorge, Rita P. Ribeiro, João Gama, Pedro Bizarro, and Rayid Ghani. Table of Contents: 1. What is Aequitas Flow? - 1.1. For Practitioners selecting a model - 1.2. For Researchers running a benchmark 2. Install Aequitas Flow 3. The components of Aequitas Flow - 3.1. Experiment - 3.2. Optimizer - 3.3. Datasets - 3.4. Methods - 3.5. Audit 4. Conclusion What is Aequitas Flow? Aequitas Flow is the codename for the latest version of Aequitas, a well-established package for fairness auditing in the ML community. This version extends the package to include experimentation with Fair ML algorithms. Aequitas started as a software to diagnose and alert for disparities in ML models' decisions depending on sensitive attributes, such as race, gender, or age. To achieve this, the package runs a Bias Audit, in which it calculates several metrics across all data groups, determined by the sensitive attributes, and compares them to identify any gap in performance. While some other packages already implement Fair ML methods, they also introduce an overhead of technical knowledge to configure, evaluate, and deploy these models. Because of this, we extended Aequitas to enable users to conduct experiments using a wide variety of fair ML methods from the literature in an intuitive user experience. Aequitas Flow was built on the principles of extensibility and reproducibility: it allows users to incorporate their own datasets and methods in the framework using familiar interfaces of sci-kit-learn and pandas. It ensures their work can be replicated by performin