FairRank is a package that quantifies bias in rankings produced by a score-based ranker, and mitigates that bias using an optimization procedure.

FairRank includes python scipts to:

(1) generate synthetic rankings while controlling the degree of bias; (2) compute bias in a ranking according to different fairness measures; (3) generate a new ranking that is as close as possible to the original ranking, yet has lower bias.

• dataGenerator.py contains the core code of the ranking generation algorithm
• measures.py contains the fairness and accuracy measures
• optimization.py implements the optimization process
• utility.py includes data transformation and ranking score generator code

runOptimization.py, runRealDataExp.py and runSyntheticExp.py usage the above core code; these scripts can be invoked on the command line

• demo_dataGenerator shows the usage of dataGenerator.py
• demo_measures shows the usage of measures.py
• demo_optimization shows the usage of runOptimization.py
• demo_realDataExp shows the usage of runRealDataExp.py
• demo_syntheticDataExp shows the usage of runSyntheticExp.py
• demo_utility shows the usage of utility.py

The above test scripts include the usage guideline of core code.

normalizer.txt stores the maximum of input population that has already computed previously. This file will be accessed during computation of normalizer of fairness measure i.e. bias in order to save time when compute the same input population multiple times. After get a normalizer of some input population, can manually add a new line into this file to save computation of normalizer during next experiment of this input population. New line should follow the below format exactly.

Format of normalizer: total user number,size of protected group,fairness measure:value of normalizer.

Example of lines are:

1000,548,rKL:100.0

1000,548,rND:100.0

1000,548,rRD:100.0

Some of real data sets we used are includes inside folder datasets.

Related documents are included inside the docs folder. To cite this work, use https://arxiv.org/abs/1610.08559