A Comparison of Fairness-Aware Machine Learning Algorithms
Haverford College. Department of Computer Science
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Fairness, as it applies to algorithms, implies that the decisions made by an algorithm are being made such that there is no discrimination against individuals or groups in labeled data sets. In this paper, I give a summary of the relevant endings by computer scientists regarding fair algorithms, discuss three techniques used to reduce/remove bias in algorithms, and examine three case studies that clearly demonstrate the importance of this field of study. The goal of this thesis is to determine the best practices for case studies on this topic and to discover ways of developing algorithms that are unbiased. I apply existing algorithms to four data sets and compare their results in order to determine which are the most useful in a specifc situation.