Learn Fair PC

This repo contains the code and experiments from the paper "Group Fairness by Probabilistic Modeling with Latent Fair Decisions", published in AAAI 2021.

Files

  baselines/    Python scripts to reproduce `Reweight`, `Reduction` and `FairLR`.
  bin/          Runnable julia scripts (see below).
  circuits/     Learned circuits in experiments.
  data/         Datasets used in the experiments.
  scripts/      Helper files to generate experiments scripts.
  src/          The source code for the algorithm.
  Project.toml  This file specifies required julia environment.
  README.md     This is this file.

Installation

1. Julia version 1.7

2. Run commands with flag --project will automatically use the packages specified in Project.toml. See belows scripts for examples.

Experiments

Usage

• Run bin/learn.jl with --help argument to see the usage message. Most of the options have default values. The following are some arguments need to be manully set:

positional arguments:
  dataset               dataset name, in {compas, adult, german, synthetic}
optional arguments:
  --sensitive_variable  sensitive variable of current data set, e.g.,{Ethnic_Code_Text_, sex, S}
  --fold                fold id for k-fold cross validation, in [1:10]
  --struct_type         indicate structure constrains of probability distributions, in {FairPC, TwoNB, NlatPC, LatNB}
  --num_X               number of non sensitive features in synthetic data set setting, in [10:30]

• Some sample scripts

$  julia --project bin/learn.jl compas --exp-id 1  --dir "exp/compas/1" --struct_type "FairPC"  --sensitive_variable "Ethnic_Code_Text_"  --fold 1 
$  julia --prroject bin/learn.jl synthetic --exp-id 2  --dir "exp/synthetic/2" --struct_type "FairPC"  --num_X 10  --sensitive_variable "S"  --fold 1 

• To generate multiple scripts and run batches of experiments in parallel, run the following for real-world dataset and synthetic dataset respectively:

$ julia --project bin/gen_exp.jl scripts/json/realworld-fair.json 
$ julia --project bin/gen_exp.jl scripts/json/synthetic-fair.json

you can also change dir in file *.json to the output directory you want.

Baselines

• For TowNB, LatNB, and NlatPC, see above.
• For Reduction, Reweight, and FairLR methods, run fair_reduction.py, reweight.py or fair_lr.py respectively(the first two in python3 and the last in python2) in directory .\baselines with the following arguments:

# usage is the same as above
positional arguments:
  dataset
optional arguments:
  --fold
  --num_X

• Some sample scripts

$ python3 reweight.py compas --fold 1
$ python2 fair_lr.py synthetic --fold 1 --num_X 30
$ python3 fair_reduction.py german --fold 2

• To generate batches scripts, run:

$ julia bin/gen_exp.jl scripts/json/baselines.json --set_id 0 --cmd python3 -b fair_reduction.py
$ julia bin/gen_exp.jl scripts/json/baselines.json --set_id 0 --cmd python3 -b reweight.py
$ julia bin/gen_exp.jl scripts/json/baselines.json --set_id 0 --cmd python -b fair_lr.py