Bernardo, Francisco. 2020. Interactive Machine Learning for User-Innovation Toolkits – An Action Design Research approach. Doctoral thesis, Goldsmiths, University of London [Thesis]

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Abstract or Description

Machine learning offers great potential to developers and end users in the creative industries.
However, to better support creative software developers' needs and empower them as machine
learning users and innovators, the usability of and developer experience with machine learning
tools must be considered and better understood. This thesis asks the following research questions:
How can we apply a user-centred approach to the design of developer tools for rapid prototyping
with Interactive Machine Learning? In what ways can we design better developer tools to accelerate
and broaden innovation with machine learning?

This thesis presents a three-year longitudinal action research study that I undertook within a
multi-institutional consortium leading the EU H2020 -funded Innovation Action RAPID-MIX. The
scope of the research presented here was the application of a user-centred approach to the design
and evaluation of developer tools for rapid prototyping and product development with machine
learning. This thesis presents my work in collaboration with other members of RAPID-MIX,
including design and deployment of a user-centred methodology for the project, interventions for
gathering requirements with RAPID-MIX consortium stakeholders and end users, and prototyping,
development and evaluation of a software development toolkit for interactive machine learning.

This thesis contributes with new understanding about the consequences and implications of a
user-centred approach to the design and evaluation of developer tools for rapid prototyping of
interactive machine learning systems. This includes 1) new understanding about the goals, needs,
expectations, and challenges facing creative machine-learning non-expert developers and 2) an
evaluation of the usability and design trade-offs of a toolkit for rapid prototyping with interactive
machine learning. This thesis also contributes with 3) a methods framework of User-Centred
Design Actions for harmonising User-Centred Design with Action Research and supporting the
collaboration between action researchers and practitioners working in rapid innovation actions,
and 4) recommendations for applying Action Research and User-Centred Design in similar contexts
and scale.

Item Type:	Thesis (Doctoral)
Keywords:	Interactive Machine Learning, End-user innovation, User-Centred Design, Action Design Research, Application Programming Interfaces, Human-Computer Interaction, Innovation Action
Departments, Centres and Research Units:	Computing
Date:	31 January 2020
Item ID:	28374
Date Deposited:	23 Apr 2020 08:06
Last Modified:	07 Sep 2022 17:15
URI:	https://research.gold.ac.uk/id/eprint/28374

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