Land & Carbon
I used open map data and machine learning to help a community in Nottingham see who owns the land and plan how to cut carbon emissions. It uses only open data and free software.
PhD Researcher at the Creative Computing Institute (CCI), University of the Arts London (UAL), working on Nature-informed Participatory AI Governance.
I'm a practice-based PhD Researcher at the CCI, UAL. I'm looking at what AI Governance can learn from how filmmakers in London governed themselves informally in the 1970s and 80s. I'm also asking whether nature can help us understand this, and apply it to AI Governance structures today.
Before my PhD, I worked as a platform and site reliability engineer at the Government Digital Service. Prior to that, I worked in several other Whitehall departments, drafting planning policy and legislation and managing a programme to sell surplus government land at train stations. I also taught in London schools with Teach First.
I live in London, but I'm happiest when I'm somewhere tropical. I'm of Jamaican and British heritage, and I'm learning Portuguese.
My PhD is practice-based, so I make as well as write. I'm asking what AI Governance can learn from the informal governance of filmmakers in London in the 1970s and 80s: how they shared work and made decisions without formal rules.
I'm also asking whether nature can help us understand this. If it can, I want to use it to design AI Governance structures that are more open to participation.
I used open map data and machine learning to help a community in Nottingham see who owns the land and plan how to cut carbon emissions. It uses only open data and free software.
A diagram showing how UK government could work with faith communities. It maps who does what, where funding could come from, and the next steps.
I led work to help a local council put its equality duties into everyday planning. This included a table of actions that helps planning officers show they are meeting the Public Sector Equality Duty at each stage.
I built a tool that draws organisation charts from the code that already lists who is in each team. On a UK government programme of over 200 people, the charts are always up to date. I used an AI tool to help write the code, and checked every result against the real team structure.