Classification systems are everywhere and this cannot be stopped. This omnipresence means they can be forgotten and hidden, and can operate without most people’s knowledge, meaning they can have important consequences that are difficult to challenge. Classification is also a key part of and use for AI systems, and these improving technologies can hide systems and consequences even further. It is hypothesised though that no matter the technology or context, the act of classification and the processes involved have similarities. In understanding this, might be able to see how and why consequences occur.
Therefore, this project will look at the entire lifecycle of classification systems – emergence, evolution, deviation, and conclusion – for two case studies. One will be at macro level of society, and the other at micro level in order to test the hypothesis that all classification processes share similarities – independent of time, purpose, and scale.
Doctoral Recognition Award Winner 2022
‘Bias’ in AI.
AI and Privacy.
Historical Precedents.
Interdisciplinary Approaches.
BSc Mathematical Sciences and History at University of Exeter, my dissertation was entitled ‘Artificial Intelligence and Slavery: the History and Mathematics of the Future’.
Prof Hilde Coffé
Dr Tom Fincham Haines
Prof David Galbreath
Prof Stuart Townley (University of Exeter)
Prof Kristofer Allerfeldt (University of Exeter)
