Open Access Te Herenga Waka-Victoria University of Wellington
thesis_access.pdf (2.31 MB)

Data Informed Decision Bias

Download (2.31 MB)
posted on 2023-11-17, 08:23 authored by Prathapa Dissanayake

Data science techniques are revolutionizing decision making processes and facilitating data driven insights. The exponential growth of data availability, coupled with advancements in computing power and algorithms, has paved the way for a data driven paradigm that is reshaping the way organizations operate. In the present thesis we discuss the use of data science techniques for decision making. We first conduct a case study of using data science techniques to reveal latent drivers for improving societal outcomes. Secondly, we reveal class imbalance issues in datasets exploited for decision-making purposes. Furthermore, we present a comprehensive discourse on discriminatory bias within the framework of machine learning algorithms. For mitigating machine learning bias, we subsequently produce novel results at the intersection of Learning Fair Representations and Variational Autoencoders. We develop a novel approach in the field of fair representation learning that demonstrates comparable or superior performance when compared to existing state-of-the-art algorithms in the domain of representation learning.


Copyright Date


Date of Award



Te Herenga Waka—Victoria University of Wellington

Rights License

Author Retains Copyright

Degree Discipline

Data Science

Degree Grantor

Te Herenga Waka—Victoria University of Wellington

Degree Level


Degree Name

Doctor of Philosophy

ANZSRC Socio-Economic Outcome code

130305 Technological ethics; 130302 Business ethics; 130306 Workplace and organisational ethics (excl. business ethics); 280115 Expanding knowledge in the information and computing sciences

ANZSRC Type Of Activity code

3 Applied research

Victoria University of Wellington Item Type

Awarded Doctoral Thesis



Victoria University of Wellington School

School of Mathematics and Statistics


Hirose, Yuichi