Fairness analysis in algorithm design

With the development of AI technology, more and more decisions are made by algorithms instead of human beings. On the one hand, machines can greatly increase working efficiency and accuracy. On the other hand, the algorithms can be designed to be more fair and ob- jective. Human beings may be subjec...

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Main Author: Guo, Yuewen
Other Authors: Bei Xiaohui
Format: Final Year Project
Language:English
Published: 2019
Subjects:
Online Access:http://hdl.handle.net/10356/77157
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-771572023-02-28T23:19:46Z Fairness analysis in algorithm design Guo, Yuewen Bei Xiaohui School of Physical and Mathematical Sciences DRNTU::Science::Mathematics With the development of AI technology, more and more decisions are made by algorithms instead of human beings. On the one hand, machines can greatly increase working efficiency and accuracy. On the other hand, the algorithms can be designed to be more fair and ob- jective. Human beings may be subjective or even having discrimination during decision making process, but with a well designed algorithm, more fair decisions can be made. In this project, we only focus on one method to mitigate discrimination, data pre-processing method. Necessarily, the definitions of fairness and sources of discrimination are discussed before the introduction of algorithms. One of the most comprehensive algorithms, Opti- mised Pre-processing method has been examined with experiments, and 5 most commonly used machine learning classification models have been built to validate the algorithm’s bias mitigation performance. Bachelor of Science in Mathematical Sciences 2019-05-14T07:52:05Z 2019-05-14T07:52:05Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/77157 en 34 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Science::Mathematics
spellingShingle DRNTU::Science::Mathematics
Guo, Yuewen
Fairness analysis in algorithm design
description With the development of AI technology, more and more decisions are made by algorithms instead of human beings. On the one hand, machines can greatly increase working efficiency and accuracy. On the other hand, the algorithms can be designed to be more fair and ob- jective. Human beings may be subjective or even having discrimination during decision making process, but with a well designed algorithm, more fair decisions can be made. In this project, we only focus on one method to mitigate discrimination, data pre-processing method. Necessarily, the definitions of fairness and sources of discrimination are discussed before the introduction of algorithms. One of the most comprehensive algorithms, Opti- mised Pre-processing method has been examined with experiments, and 5 most commonly used machine learning classification models have been built to validate the algorithm’s bias mitigation performance.
author2 Bei Xiaohui
author_facet Bei Xiaohui
Guo, Yuewen
format Final Year Project
author Guo, Yuewen
author_sort Guo, Yuewen
title Fairness analysis in algorithm design
title_short Fairness analysis in algorithm design
title_full Fairness analysis in algorithm design
title_fullStr Fairness analysis in algorithm design
title_full_unstemmed Fairness analysis in algorithm design
title_sort fairness analysis in algorithm design
publishDate 2019
url http://hdl.handle.net/10356/77157
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