A General Framework for Fair Regression

A General Framework for Fair Regression

Fairness, through its many forms and definitions, has become an important issue facing the machine learning community. In this work, we consider how to incorporate group fairness constraints into kernel regression methods, applicable to Gaussian processes, support vector machines, neural network reg...

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Bibliographic Details
Main Authors: Jack Fitzsimons, AbdulRahman Al Ali, Michael Osborne, Stephen Roberts
Format: Article
Language:English
Published: MDPI AG 2019-07-01
Series:Entropy
Subjects:
machine learning
algorithmic fairness
kernel methods
constrained learning
Gaussian process
decision tree
neural network
Online Access:https://www.mdpi.com/1099-4300/21/8/741

Internet

https://www.mdpi.com/1099-4300/21/8/741

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