Ranking for Individual and Group Fairness Simultaneously (FIGR).

This repository consists of code and datasets for our paper. We extend the code of DELTR and FA*IR to compare our work with these algorithms as baselines.

Requirements

octave-general and octave-parallel

Fair LTR on the ChileSAT dataset

Preprocessing training data

The bashscript in the root folder pre-processes the ChileSAT data with FIGR and FA*IR

preprocess.sh pre-processes the training data.

Model Training

For ChileSAT dataset a bash-script named trainEngineering.sh is available that trains models for all experimental settings and saves them into results/EngineeringStudents/NoSemiPrivate/PROTECTED-ATTRIBUTE/FOLD/EXPERIMENTAL-SETTING/model.m.

Training parameters like learning rate or number of iterations can be changed in listnet-src/globals.m and deltr-src/globals.m.

The parameter \gamma for DELTR is set in a command line argument. The parameter k for FIGR is also set in a command line arguments.

Predictions

For the ChileSAT a bash-script named predictEngineering.sh is available, that uses the previously trained models and testdata to predict rankings. Predictions are stored in the same folder as model.m.

This script also copies the prediction files from the DELTR experiment with Gamma=0 (color-aware LTR) into folders EngineeringStudents/NoSemiPrivate/FA-IR/P-VALUE, EngineeringStudents/NoSemiPrivate/FIGR, EngineeringStudents/NoSemiPrivate/FIGR_PLUS, EngineeringStudents/NoSemiPrivate/FIGR_MINUS and , which are then needed by the post-processing script.

Post-Processing

The root directory contains bash-script for post-processing LTR predictions on ChileSAT test data with FIGR and also FA*IR (Baseline)

postprocess.sh

Directly applying fair post-processing (re-ranking) methods on the German credit risk and the COMPAS recidivism datasets

post-processing the true ranking in the dataset.

This bash-script re-ranks the true rankig using FIGR and also FA*IR (Baseline)

postprocessTrueRanking.sh

Result Evaluation

The bash-script evaluates results on all three datasets.

evaluateResults.sh