Fairness.jl

Fairness.jl is a comprehensive bias audit and mitigation toolkit in julia. Extensive support and functionality provided by MLJ has been used in this package.

For an introduction to Fairness.jl refer the notebook available at https://nextjournal.com/ashryaagr/fairness

⚠️ This is experimental software: We refer Aequitas for stable bias auditing.

Installation

using Pkg
Pkg.activate("my_environment", shared=true)
Pkg.add("Fairness")
Pkg.add("MLJ")

What Fairness.jl offers over its alternatives?

• As of writing, it is the only bias audit and mitigation toolkit to support data with multi-valued protected attribute. For eg. If the protected attribute, say race has more than 2 values: "Asian", "African", "American"..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. Example is available in Documentation
• Due to the support for multi-valued protected attribute, 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 the attributes like "female_american", "male_asian"...so on.
• Extensive support and functionality provided by MLJ can be leveraged when using Fairness.jl
• Tuning of models using MLJTuning from MLJ. Numerious ML models from MLJModels can be used together with Fairness.jl
• It leverages the flexibility and speed of Julia to make it more efficient and easy-to-use at the same time
• Well structured and intutive design
• Extensive tests and Documentation

Getting Started

• Documentation is a good starting point for this package.
• To understand Fairness.jl, it is recommended that the user goes through the MLJ Documentation. It shall help the user in understanding the usage of machine, evaluate, etc.
• Incase of any difficulty or confusion feel free to open an issue.

Example

Following is an introductory example of using Fairness.jl. Observe how easy it has become to measure and mitigate bias in Machine Learning algorithms.

using Fairness, MLJ
X, y, ŷ = @load_toydata

julia> model = ConstantClassifier()
ConstantClassifier() @904

julia> wrappedModel = ReweighingSamplingWrapper(classifier=model, grp=:Sex)
ReweighingSamplingWrapper(
    grp = :Sex,
    classifier = ConstantClassifier(),
    factor = 1) @312

julia> evaluate(
          wrappedModel,
          X, y,
          measures=MetricWrappers(
              [true_positive, true_positive_rate], grp=:Sex))
┌────────────────────┬─────────────────────────────────────────────────────────────────────────────────────┬───────────────────────────────────── ⋯
│ _.measure          │ _.measurement                                                                       │ _.per_fold                           ⋯
├────────────────────┼─────────────────────────────────────────────────────────────────────────────────────┼───────────────────────────────────── ⋯
│ true_positive      │ Dict{Any,Any}("M" => 2,"overall" => 4,"F" => 2)                                     │ Dict{Any,Any}[Dict("M" => 0,"overall ⋯
│ true_positive_rate │ Dict{Any,Any}("M" => 0.8333333333333334,"overall" => 0.8333333333333334,"F" => 1.0) │ Dict{Any,Any}[Dict("M" => 4.99999999 ⋯
└────────────────────┴─────────────────────────────────────────────────────────────────────────────────────┴───────────────────────────────────── ⋯

Components

Fairness.jl is divided into following components

FairTensor

It is a 3D matrix of values of TruePositives, False Negatives, etc for each group. It greatly helps in optimization and removing the redundant calculations.

Measures

CalcMetrics

FairMetrics

BoolMetrics [WIP]

These metrics shall use either parity or shall have custom implementation to return boolean values

Fairness Algorithms

These algorithms are wrappers. These help in mitigating bias and improve fairness.

Contributing

• Various Contribution opportunities are available. Some of the possible contributions have been listed at the pinned issue
• Feel free to open an issue or contact on slack. Let us know where your intersts and strengths lie and we can find possible contribution opportunities for you.

Citing Fairness.jl

@software{ashrya_agrawal_2020_3977197,
  author       = {Ashrya Agrawal and
                  Jiahao Chen and
                  Sebastian Vollmer and
                  Anthony Blaom},
  title        = {ashryaagr/Fairness.jl},
  month        = aug,
  year         = 2020,
  publisher    = {Zenodo},
  version      = {v0.1.2},
  doi          = {10.5281/zenodo.3977197},
  url          = {https://doi.org/10.5281/zenodo.3977197}
}