Persons, people and personalisation: understanding subjectivity through AI

AI-driven personalisation offers a clear opportunity for creative industries to more effectively engage audiences. But personalisation is reliant on profiles created from individual and collective data, raising questions as to what gaps exist between the models and human experience. This project will partner with a world-leading television and multiplatform company to understand how such personalisation can be effectively and ethically exploited in story experiences to generate greater audience engagement. It addresses a clear creative industries business challenge with clear commercial returns.
Living human beings generate data that in turn feeds, together with other people's data and models, the profile-creation and various other methods through which machine learning techniques personalise content. How are living human beings mapped onto the profiles and personas used for personalisation? How well does it work? Are there gaps between the 'models' of the humans and their groups, which broadly understood AI generate, and the experiences of the living human beings themselves, when they receive their personalised content?
This PhD focuses on personalisation and asks a range of questions, from conceptual ones, about the techniques of personalisation both in terms of machine learning innovation and their cultural effects, to the practical ones, on how specific audiences respond to personalised content. The overall aim of the project will be to understand how effectively a 'subjectivity' modelled by AI works with human experiences, conceptually, technologically, ethically, and in terms of personalised cultural content.
The methodology for this project will draw on digital and cultural theory, ethics, audience research and Design Science Research (DSR). Using mixed methods is the most fitting approach for an interdisciplinary PhD. DSR will be especially useful in that it partners critical/theoretical reflection with a practical approach to delivering value to our partner via its: (a) iterative nature, promoting continuous learning between problems and solutions; (b) focus on utility, ensuring that the outcomes are of practical economic benefit; (c) ability to both integrate theory from reference disciplines and generate new theory via the design process; and (d) focus on improving a knowledge base via reflection on practice and generation of innovation. We anticipate that this project will produce results that will fundamentally challenge the way that personalisation is conceived of.