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Fairness-aware machine learning: algorithms, comparisons, bechmarking

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 run the benchmarks, clone the repository and run:

$ python3

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

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Files for fairness, version 0.0.9
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