Understanding and mitigating unintended demographic bias in machine learning systems
Author(s)Sweeney, Christopher(Christopher J.),M. Eng.Massachusetts Institute of Technology.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Richard Fletcher and Maryam Najaan.
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Machine Learning is becoming more and more influential in our society. Algorithms that learn from data are streamlining tasks in domains like employment, banking, education, heath care, social media, etc. Unfortunately, machine learning models are very susceptible to unintended bias, resulting in unfair and discriminatory algorithms with the power to adversely impact society. This unintended bias is usually subtle, emanating from many different sources and taking on many forms. This thesis will focus on understanding how unfair biases with respect to various demographic groups show up in machine learning systems. Furthermore, we develop multiple techniques to mitigate unintended demographic bias at various stages of typical machine learning pipelines. Using Natural Language Processing as a framework, we show substantial improvements in fairness for standard machine learning systems, when using our bias mitigation techniques.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 81-84).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.