A module which fairly distributes a list of arbitrary objects among a set of targets, considering weights.
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Updated
Jun 20, 2017 - Python
A module which fairly distributes a list of arbitrary objects among a set of targets, considering weights.
An implementation of the Core and Subcore protocols proposed by Aziz and Mackenzie (2016) for discrete, bounded, envy-free, partial division of a heterogeneous resource
Introduction to Provably Fair Gaming Algorithms
💬 Talk on "Fair Inference on Outcomes" (R. Nabi & I. Shpitser, 2017), for M. Hardt's "Fairness in Machine Learning" seminar at Berkeley, Fall 2017
A collection of implementations of fair ML algorithms
Our scientific ethos, our scientific community at the Institute of Philosophy at the University of Stuttgart
Python code for training fair logistic regression classifiers.
It's a modified version of hadoop yarn resource manager. It has a new fair(drf) scheduling + simulated annealing policy that has been added to the package. visit below for more information.
Tool to visualize and diagnose fairness issues in machine learning
🗝An interactive piece on the different types of fairness.
Code and data for "Enhancing the Accuracy and Fairness of Human Decision Making", NeurIPS 2018
Presentation for a brief talk at URI about ethics in data science
A novel modification to existing k-shortest path algorithms which re-routes drivers with regard to reducing average travel time whilst maintaining fairness
Building Fair AI models tutorial at PyData Berlin / REVISION 2018
Tackling Gender and Race bias in Word Embeddings
Asynchronous distributed pricing, is an algorithm to make a fairness between wireless network.
Fairness-aware Data Mining
A method to preprocess the training data, producing an adjusted dataset that is independent of the group variable with minimum information loss.
Fairness in ML using adversarial training.
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