FairCAIPI

FairCAIPI extends CAIPI (Teso and Kersting, 2019) by a fairness objective. It is a novel bias mitigation human-in-the-loop strategy that involves users by fairly correcting local explanations of ML models decisions.

Install

FairCAIPI was developed in Python3 and only the following versions were tested: 3.10.7 3.8.10 FairCAIPI uses the frameworks aif360, modAL, and SHAP.

Install Required Packages

• pip install -r requirements.txt

Download Dataset

• follow the instructions in lib/$YOUR_PYTHON_VERSION/site-packages/aif360/data/raw/german/README.md
  • wget -O $VENV_NAME/lib/$YOUR_PYTHON_VERSION/site-packages/aif360/data/raw/german/german.data "https://archive.ics.uci.edu/ml/machine-learning-databases/statlog/german/german.data"
  • wget -O $VENV_NAME/lib/$YOUR_PYTHON_VERSION/site-packages/aif360/data/raw/german/german.doc "https://archive.ics.uci.edu/ml/machine-learning-databases/statlog/german/german.doc"

Usage

Run FairCAIPI as follows:

usage: main.py [-h] [--seed ?] [--Z Z] [--min_perc MIN_PERC] [--T T]
               {interactive,simulation,plot,default_rf,reweighed_rf} ...

Fair Interacting with Explanations

positional arguments:
  {interactive,simulation,plot,default_rf,reweighed_rf}
                        sub commands
    interactive         run interactive mode
    simulation          run simulation mode
    plot                print plots from existing csv-file
    default_rf          show evaluation results for default Random Forest
    reweighed_rf        show evaluation results for reweighed Random Forest

optional arguments:
  -h, --help            show this help message and exit
  --seed ?              seed to use for randomization
  --Z Z                 threshold under which the accuracy should not fall
                        during the interaction process for orientation
  --min_perc MIN_PERC   minimum percentage to determine contribution threshold
                        of protected attribute Shapley value
  --T T                 number of iterations

Run in interactive mode

usage: main.py interactive [-h]

optional arguments:
  -h, --help  show this help message and exit

Run in simulation mode

usage: main.py simulation [-h] [--print_plot ?]

optional arguments:
  -h, --help      show this help message and exit
  --print_plot ?  print plot of data generated during simulation

Print plots from CSV-file

usage: main.py plot [-h] --plot_file ?

optional arguments:
  -h, --help     show this help message and exit
  --plot_file ?  path to csv-file

Show evaluation results for Default Random Forest

usage: main.py default_rf [-h]

optional arguments:
  -h, --help  show this help message and exit

Show evaluation results for Reweighed Random Forest

usage: main.py reweighed_rf [-h]

optional arguments:
  -h, --help  show this help message and exit