Skip to main content

Fairness-aware machine learning: algorithms, comparisons, benchmarking

Project description

This repository is meant to facilitate the benchmarking of fairness aware machine learning algorithms.

The associated paper is:

A comparative study of fairness-enhancing interventions in machine learning by Sorelle A. Friedler, Carlos Scheidegger, Suresh Venkatasubramanian, Sonam Choudhary, Evan P. Hamilton, and Derek Roth.

To install this software run:

$ pip3 install fairness

The below instructions are still in the process of being updated to work with the new pip install-able version.

To run the benchmarks:

$ from fairness.benchmark import run
$ run()

This will write out metrics for each dataset to the results/ directory.

To generate graphs and other analysis run:

$ python3

If you do not yet have all the packages installed, you may need to run:

$ pip install -r requirements.txt

Optional: The benchmarks rely on preprocessed versions of the datasets that have been included in the repository. If you would like to regenerate this preprocessing, run the below command before running the benchmark script:

$ python3

To add new datasets or algorithms, see the instructions in the readme files in those directories.

OS-specific things

On Ubuntu

(We tested on Ubuntu 16.04, your mileage may vary)

You'll need python3-dev:

$ sudo apt-get install python3-dev

Additional analysis-specific requirements

To regenerate figures (this is messy right now. we're working on it)

Python requirements (use pip):

  • ggplot

System requirements:

R package requirements (use install.packages):

  • rmarkdown
  • stringr
  • ggplot2
  • dplyr
  • magrittr
  • corrplot
  • robust

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for fairness, version 0.1.8
Filename, size File type Python version Upload date Hashes
Filename, size fairness-0.1.8-py3-none-any.whl (14.2 MB) File type Wheel Python version py3 Upload date Hashes View
Filename, size fairness-0.1.8.tar.gz (12.9 MB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page