Data science and usefulness in domains of human action
Date
2023
Authors
Andreacchio, Anton
Editors
Advisors
Bean, Nigel
Mitchell, Lewis
Mitchell, Lewis
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Thesis
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Abstract
Rapid advancements in computational science and artificial intelligence are transforming
virtually every industry. In many situations however, uninterpretable modelling techniques
are challenging to implement, and raise significant governance, operational and ethical
challenges.
In this thesis, we focus on three domains where there is limited data availability,
significant complexity, and human decision makers. Rather than focusing on increasing
data collection in these domains, we focus on developing useful models based on readily
available data, describing a model’s usefulness as being based on five characteristics of
interest: performance, scalability, comprehensibility, justifiability and actionability.
In Chapter 3, we model Australian Rules Football as spatial systems rather than
individual possession events. Several methods are introduced to disentangle relative team
performance and the functioning of sub-systems to evaluate historical games and predict
future performance.
Next, Chapter 4 explores startup transformation pathways in South Australia. Working
with limited data in the South Australian startup ecosystem to map startup capitalisation,
we follow 151 startup journeys over an eight year period to develop an approach to support
policy-makers to understand ecosystem transformation, with a focus on grant interventions
and private capitalisation events.
In Chapter 5, we explore creativity and the writers room, working alongside the TV
series Aftertaste to evaluate the limits and potential for natural language processing to
support the creative process. By approaching the intersection of creativity and data
analysis from the direction of usefulness, we are able to evaluate an existing method for
story arc generation and rethink the approach to make it a more useful tool to support
creative development.
Finally, we conclude in Chapter 6 by discussing our results and the role of data science
modelling in these three domains. This includes a summary of results across the three
domains, the relationship between governance and data science projects, and areas for
further research in each direct domain.
This work presents advances in each of the three domains explored, presenting new
practical approaches as well as revealing significant new areas for further research. In
addition, the work demonstrates the viability of usefulness characteristics for data science research, with positive implications for governance, research, and development of
complementary techniques to uninterpretable artificial intelligence and machines learning
methods.
School/Discipline
School of Mathematical Sciences
Dissertation Note
Thesis (M.Phil.) -- University of Adelaide, School of Mathematical Sciences, 2023
Provenance
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