Fairness.jl

Julia Toolkit with fairness metrics and bias mitigation algorithms
Author ashryaagr
Popularity
18 Stars
Updated Last
1 Year Ago
Started In
May 2020

Fairness.jl

Build Status Build status Coverage Status #mlj Documentation

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

Name Metric Instances
True Positive truepositive, true_positive
True Negative truenegative, true_negative
False Positive falsepositive, false_positive
False Negative falsenegative, false_negative
True Positive Rate truepositive_rate, true_positive_rate, tpr, recall, sensitivity, hit_rate
True Negative Rate truenegative_rate, true_negative_rate, tnr, specificity, selectivity
False Positive Rate falsepositive_rate, false_positive_rate, fpr, fallout
False Negative Rate falsenegative_rate, false_negative_rate, fnr, miss_rate
False Discovery Rate falsediscovery_rate, false_discovery_rate, fdr
Precision positivepredictive_value, positive_predictive_value, ppv
Negative Predictive Value negativepredictive_value, negative_predictive_value, npv

FairMetrics

Name Formula Value for Custom function (func)
disparity metric(Gᵢ)/metric(RefGrp) ∀ i func(metric(Gᵢ), metric(RefGrp)) ∀ i
parity [ (1-ϵ) <= dispariy_value[i] <= 1/(1-ϵ) ∀ i ] [ func(disparity_value[i]) ∀ i ]

BoolMetrics [WIP]

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

Metric Aliases
Demographic Parity DemographicParity

Fairness Algorithms

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

Algorithm Name Metric Optimised Supports Multi-valued protected attribute Type Reference
Reweighing General ✔️ Preprocessing Kamiran and Calders, 2012
Reweighing-Sampling General ✔️ Preprocessing Kamiran and Calders, 2012
Equalized Odds Algorithm Equalized Odds ✔️ Postprocessing Hardt et al., 2016
Calibrated Equalized Odds Algorithm Calibrated Equalized Odds Postprocessing Pleiss et al., 2017
LinProg Algorithm Any metric ✔️ Postprocessing Our own Algorithm
Penalty Algorithm Any metric ✔️ Inprocessing Our own Algorithm

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

DOI

@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}
}