# Fairness.jl : Bias Audit and Mitigation Toolkit

**URL:** <https://discourse.julialang.org/t/fairness-jl-bias-audit-and-mitigation-toolkit/43832>\
**Category:** Package Announcements\
**Tags:** package, announcement\
**Created:** [July 28, 2020, 7:25pm UTC](https://discourse.julialang.org/t/fairness-jl-bias-audit-and-mitigation-toolkit/43832 "2020-07-28T19:25:46Z")\
**Posts on this page:** 3\
**Page:** 1

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**Author:** ![ashryaagr](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/ashryaagr/32/14570_2.png) [@ashryaagr](https://discourse.julialang.org/u/ashryaagr)\
**Post date:** [July 28, 2020, 7:25pm UTC](https://discourse.julialang.org/t/fairness-jl-bias-audit-and-mitigation-toolkit/43832/1 "2020-07-28T19:25:46Z")

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I am glad to announce the first official release of [Fairness.jl](https://github.com/ashryaagr/Fairness.jl) : A Comprehensive Bias Audit and Mitigation Toolkit in Julia designed with the aim to solve practical problems faced by practitioners with existing toolkits.

It is also the first Fairness Toolkit of Julia!!

**Blog Post on Introduction to Fairness.jl:** [Fairness.jl - Fairness Toolkit in Julia - Nextjournal](https://nextjournal.com/ashryaagr/fairness)

**Docs:** [Fairness.jl](https://www.ashrya.in/Fairness.jl/dev/)

**Github Repository:** [GitHub - ashryaagr/Fairness.jl: Julia Toolkit with fairness metrics and bias mitigation algorithms](https://github.com/ashryaagr/Fairness.jl)

As of writing, the package is registered in Julia General registry, with version 0.1.1

```julia
]add Fairness

```

* * *

This Toolkit offers a number of unique features over the fairness toolkits in other languages such as Python, R, etc. :

- As of writing, it is the only bias audit and mitigation toolkit to support data with multi-valued protected attributes. For eg. If the protected attribute, say race has more than 2 values: “Asian”, “African”, “American” and so on, then Fairness.jl can easily handle it with normal workflow.

- Multiple Fairness algorithms can be applied at the same time by wrapping the wrapped Model.

- Due to the support for multi-valued protected attributes, intersectional fairness can also be dealt with this toolkit. For eg. If the data has 2 protected attributes, say race and gender, then Fairness.jl can be used to handle it by combining attributes like “female\_american”, “male\_asian” and so on.

- Extensive support and functionality provided by [MLJ](https://github.com/alan-turing-institute/MLJ.jl) can be leveraged when using Fairness.jl

- It leverages the flexibility and speed of Julia to make it more efficient and easy-to-use at the same time

- The concept of Fairness Tensor has been used to optimize the package.

Requests/suggestions, PRs are more than welcome 🙂

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<div class="post-metadata">

**Author:** ![SteffenPL](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/steffenpl/32/206270_2.png) [@SteffenPL](https://discourse.julialang.org/u/SteffenPL)\
**Post date:** [July 30, 2020, 10:06am UTC](https://discourse.julialang.org/t/fairness-jl-bias-audit-and-mitigation-toolkit/43832/2 "2020-07-30T10:06:29Z")

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The package looks great!

I was actually working in the last two weeks on a similar package, but without reaching such a good state like yours. I will see if I can make some PRs to your package!

Which resources would you recommend to get an overview over “data fairness”?

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**Author:** ![ashryaagr](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/ashryaagr/32/14570_2.png) [@ashryaagr](https://discourse.julialang.org/u/ashryaagr)\
**Post date:** [July 30, 2020, 6:18pm UTC](https://discourse.julialang.org/t/fairness-jl-bias-audit-and-mitigation-toolkit/43832/3 "2020-07-30T18:18:13Z")

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It is great to see that you are interested in contributing to the project!! Feel free to discuss the possible contributions. Some possible contributions have been listed [the pinned issue](https://github.com/ashryaagr/Fairness.jl/issues/3#issuecomment-656812338).

To get an overview of data fairness, I recommend the following resources:

- [FAT 2019 Tutorial by Microsoft Research](https://drive.google.com/file/d/1rUQkVS0NzSH3IEqZDsczSxBbhYHbjamN/view)
- [Slides of Cornell’s Machine Learning course for the lecture on Fairness in ML](http://www.cs.cornell.edu/courses/cs4786/2019sp/lectures/lec25.pdf)
- Sone talks from ACM FAT Conferences would also be helpful to get an overview of Data Fairness.

But the package and the documentation has been designed in a manner that one doesn’t need to have much knowledge about Fairness. One can easily learn other fairness concepts as he progresses through the documentation and algorithms in the package.