Fairness in Machine Learning

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Pourebrahim, Tayeb | 2022

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 57061 (02)
University: Sharif University of Technology
Department: Mathematical Sciences
Advisor(s): Foroughmand Araabi, Mohammad Hadi
Abstract:
As machine learning continues to be used extensively in all aspects of human life, especially social and legal decision making; Concerns have been raised about data-driven software and services biasing against certain demographic groups. Machine learning fairness, which refers to methods for correcting algorithmic bias in automated decision-making systems, is not only a social concern but also an industry need for developing human-centered tools.The study reviews studies on bias, fairness definitions, and attempts to reduce bias in machine learning models. Eventually, we suggest a method for reducing bias in imbalanced datasets
Keywords:
Imbalanced Data ; Machine Learning ; Deep Learning ; Fairness in Artificial Intelligence ; Bias in Artificial Intelligence