27 Open Source Bias Software Projects
Free and open source bias code projects including engines, APIs, generators, and tools.
Charlax Engineering Management 1641 ⭐
A collection of inspiring resources related to engineering management and tech leadership
Aif360 1096 ⭐
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
Awesome Concepts 312 ⭐
Awesome list about all kinds of interesting topics: Laws, Principles, Mental Models, Cognitive Biases
Blindpad 187 ⭐
Collaborative text editor (like Google Docs or CoderPad) with integrated semi-anonymizing voice chat intended to help reduce bias in technical communication.
Unbiased_lambdamart 158 ⭐
Code for WWW'19 "Unbiased LambdaMART: An Unbiased Pairwise Learning-to-Rank Algorithm", which is based on LightGBM
25daysinmachinelearning 47 ⭐
I will update this repository to learn Machine learning with python with statistics content and materials
Machine Learning Ethics References 35 ⭐
List of references about Machine Learning and Data Science bias and ethics
Responsibly 35 ⭐
Toolkit for Auditing and Mitigating Bias and Fairness of Machine Learning Systems 🔎🤖🧰
Facerec Bias Bfw 19 ⭐
Source code and notebooks to reproduce experiments and benchmarks on Bias Faces in the Wild (BFW).
Misinfo 17 ⭐
📊 Tools to Perform ‘Misinformation’ Analysis on a Text Corpus (wrapper for methods in https://github.com/PDXBek/Misinformation)
Word2vec Bias Extraction 15 ⭐
How are words loaded with meaning? Repository to accompany research paper in preparation by Alina Arseniev-Koehler and Jacob G. Foster, titled "Machine learning as a model for cultural learning: teaching an algorithm what it means to be fat."
Null 26 ⭐
A collection of news articles, books, and papers on Responsible AI cases. The purpose is to study these cases and learn from them to avoid repeating the failures of the past.
Null 11 ⭐
This python script guesses the race and gender (probabilistically) of the first and last authors for papers in your citation list (in the form of a .bib file) and compares your list to expected distributions based on a model that accounts for paper characteristics (e.g., author location, author seniority, journal, et cetera.) unrelated to race and gender.