University of Oulu

A survey in fairness in classification based machine learning

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Author: Kivilahti, Veli1
Organizations: 1University of Oulu, Faculty of Information Technology and Electrical Engineering, Information Processing Science
Format: ebook
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.6 MB)
Pages: 23
Persistent link:
Language: English
Published: Oulu : V. Kivilahti, 2023
Publish Date: 2023-08-17
Thesis type: Bachelor's thesis
Tutor: Turhan, Burak


As the usage and impact of machine learning applications increase, it is increasingly important to ensure that the systems in use are beneficial to users and larger society around them. One of steps to ensure this is limiting unfairness that the algorithm might have. Existing machine learning applications have sometimes shown that they have been disadvantageous to certain minorities and to combat this we have a need for defining what does fairness means, and how can we increase it in our machine learning applications. The survey is done as a literary review with the goal of presenting an overview of fairness in classification-based machine learning. The survey goes through the motivation for fairness briefly through philosophical background and examples of unfairness and goes through the most popular fairness definitions in machine learning. After this the paper lists some of the most important methods for restricting unfairness splitting the methods into pre- in- and post-processing methods.

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Copyright information: © Veli Kivilahti, 2023. Except otherwise noted, the reuse of this document is authorised under a Creative Commons Attribution 4.0 International (CC-BY 4.0) licence ( This means that reuse is allowed provided appropriate credit is given and any changes are indicated. For any use or reproduction of elements that are not owned by the author(s), permission may need to be directly from the respective right holders.