Fairness in computational decision making

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http://urn.fi/URN:NBN:fi:hulib-202012084729
Title: Fairness in computational decision making
Author: Saarinen, Tuomo
Contributor: University of Helsinki, Faculty of Science
Publisher: Helsingin yliopisto
Date: 2020
Language: eng
URI: http://urn.fi/URN:NBN:fi:hulib-202012084729
http://hdl.handle.net/10138/322459
Thesis level: master's thesis
Discipline: Tietojenkäsittelytiede
Abstract: The use of machine learning and algorithms in decision making processes in our every day lifehas been growing rapidly. The uses range from bank loans and taxation to criminal sentencesand child care decisions. Because of the possible high importance of such decisions, we need tomake sure that the algorithms used are as unbiased as possible.The purpose of this thesis is to provide an overview of the possible biases in algorithm assisteddecision making, how these biases affect the decision making process, and go through someproposes on how to tackle these biases. Some of the proposed solutions are more technical,including algorithms and different ways to filter bias from the machine learning phase. Othersolutions are more societal and legal and address the things we need to take into account whendeciding what can be done to reduce bias by legislation or by enlightening people on the issuesof data mining and big data.
Subject: machine learning
fairness
transparency


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