Algorithmic discrimination: How do big data tools unwittingly cause disparity?

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School of Business | Bachelor's thesis
Degree programme
Tieto- ja palvelujohtaminen
The use of big data and machine learning tools are becoming increasingly commonplace within the decision-making processes of our socio-economic interactions. In the real world, outcomes are rarely binary with distinct ‘yes’ and ‘no’ answers. Therefore, the subjectivity involved in any decision-making interaction brings about questions on fairness. These same difficulties regarding fairness are equally present within the realm of machine learning and algorithmic decision-making. The research question: “How Do Big Data Tools Unwittingly Cause Disparity?” will be studied through two narrower sub-questions: 1. “What components of algorithmic decision-making are discriminatory” and 2. “To what extent should auditing take place in algorithmic decision-making processes”. Through the form of an integrative literature review, this thesis is an overview on the topic of algorithmic discrimination attempting to contribute to answering the questions above. After identifying and researching seminal works in the field of algorithmic bias, this paper will achieve three learning goals. This thesis firstly aims to synthesize research on the main causes for algorithmic discrimination, subsequently to critique the applicability of current anti-discrimination legislation in context of big data and machine learning. Finally, this thesis will explore potential courses of action to both prevent and purge these systems of discrimination. The results of this research paper contribute to generating awareness surrounding algorithmic discrimination. Additionally, the findings encourage regulatory reform through the explorations of the necessity for responsibility and accountability for the various parties involved including government, private firms and data collectors, and users.
Thesis advisor
Hekkala, Riitta
big data, discrimination, algorithm, informed decision-making, data mining