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https://github.com/kyrcha/awesome-algofairness

A curated list of awesome algorithmic fairness resources.
https://github.com/kyrcha/awesome-algofairness

List: awesome-algofairness

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A curated list of awesome algorithmic fairness resources.

Host: GitHub
URL: https://github.com/kyrcha/awesome-algofairness
Owner: kyrcha
Created: 2019-09-06T09:48:14.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2019-09-05T06:21:06.000Z (over 5 years ago)
Last Synced: 2024-10-30T06:56:56.354Z (about 2 months ago)
Homepage:
Size: 14.6 KB
Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- Contributing: contributing.md
- Code of conduct: code-of-conduct.md

Awesome Lists containing this project

ultimate-awesome - awesome-algofairness - A curated list of awesome algorithmic fairness resources. (Other Lists / PowerShell Lists)

README

        # Awesome Algorithmic Fairness [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)

> A curated list of awesome algorithmic fairness resources.

## Introduction

- [Machine Bias](https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing)

- [Tutorial: 21 fairness definitions and their politics](https://youtu.be/jIXIuYdnyyk)

- [NIPS 2017 Fairness In Machine Leaning](https://nips.cc/Conferences/2017/Schedule?showEvent=8734). ([Video](https://vimeo.com/248490141)), ([Slides](http://fairml.how/tutorial/#/))

- [A Tutorial on Fairness in Machine Learning](https://towardsdatascience.com/a-tutorial-on-fairness-in-machine-learning-3ff8ba1040cb)

- [Attacking discrimination with smarter machine learning](http://research.google.com/bigpicture/attacking-discrimination-in-ml/)

- [Google Developers ML Fairness Overview](https://developers.google.com/machine-learning/fairness-overview/)

- [Google Developers ML Crash Course Fairness](https://developers.google.com/machine-learning/crash-course/fairness/video-lecture)

## Tutorials

* [AIF360 Tutorials](http://aif360.mybluemix.net/resources#tutorials)

## Guideline, Principle and Practices

* [Google AI - Responsible AI Practices](https://ai.google/education/responsible-ai-practices)

## Selection of research papers

## Software and data sets

### Visualization

* [Facets](https://pair-code.github.io/facets/)

### Python

* [Fairness comparison](https://github.com/algofairness/fairness-comparison)

* [AI Fairness 360 Open Source Toolkit](http://aif360.mybluemix.net/)

* [BlackBoxAuditing](https://github.com/algofairness/BlackBoxAuditing)

* [Themis ML](https://github.com/cosmicBboy/themis-ml)

* [FairML](https://github.com/adebayoj/fairml)

* [Themis](https://github.com/LASER-UMASS/Themis)

* [FairSquare](https://github.com/sedrews/fairsquare)

* [FairTest](https://github.com/columbia/fairtest)

* [Aequitas](https://github.com/sakshiudeshi/Aequitas)

* [Aequitas](https://dsapp.uchicago.edu/projects/aequitas/)

* [AuditAI](https://github.com/pymetrics/audit-ai)

* [What If Tool](https://github.com/tensorflow/tensorboard/tree/master/tensorboard/plugins/interactive_inference)

## Datasets

* [COMPAS Recidivism Risk](https://www.propublica.org/datastore/dataset/compas-recidivism-risk-score-data-and-analysis) ([GitHub](https://github.com/propublica/compas-analysis))

* [Statlog German Credit Data](https://archive.ics.uci.edu/ml/datasets/Statlog+(German+Credit+Data))

* [Default of credit card clients](https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients)

* [Census Income](http://archive.ics.uci.edu/ml/datasets/Census+Income) (same as [Adult](https://archive.ics.uci.edu/ml/datasets/adult))

* [SAT](http://www.fairness-measures.org/Pages/Datasets/SAT.html)

* [PSU-Chile](http://www.fairness-measures.org/Pages/Datasets/SATChile.html)

* [SOEP](http://www.fairness-measures.org/Pages/Datasets/SOEP.html)

* [MEPS](https://meps.ahrq.gov/mepsweb/) (Medical Expenditure Panel Survey)

* [The Dutch Virtual Census of 2001 - IPUMS Subset](https://microdata.worldbank.org/index.php/catalog/2102/study-description)

* [Law School Admissions Council’s National Longitudinal Bar Passage Study](https://files.eric.ed.gov/fulltext/ED469370.pdf)

## Organizations, workgroups and conferences

### Corporation research programs

* IBM [Trusted AI](https://www.research.ibm.com/artificial-intelligence/trusted-ai/)

* Google Brain [Pair (People + AI Research)](https://ai.google/research/teams/brain/pair)

### Academic research centers

* The University of Chicago [Center of Data Science and Public Policy](https://dsapp.uchicago.edu)

* UC Berkeley [Center for Technology, Society & Policy](https://ctsp.berkeley.edu/) [Algorithmic Fairness and Opacity Worknig Group](http://afog.berkeley.edu/)

* Harvard [Algorithmic Fairness](http://fairness.haverford.edu/)

* MIT [Active Fairness in Algorithmc Decision Making](https://www.media.mit.edu/projects/active-fairness/)

* [AI Now Institute](https://ainowinstitute.org/)

### Activist groups

* [AlgorithmWatch](AlgorithmWatch)

### Conferences and workshops

* [ACM FAT*](https://fatconference.org/)

* [AIES](http://www.aies-conference.com/)

* [AI Now Symposium](https://symposium.ainowinstitute.org/)

* [FAT/ML](http://www.fatml.org/)

* [FairWare](http://fairware.cs.umass.edu/index.html)

## Contribute

Contributions welcome! Read the [contribution guidelines](contributing.md) first.

## License

[![CC0](http://mirrors.creativecommons.org/presskit/buttons/88x31/svg/cc-zero.svg)](http://creativecommons.org/publicdomain/zero/1.0)

To the extent possible under law, Pomin Wu has waived all copyright and

related or neighboring rights to this work.