Projects

A research programme, not a portfolio.

Each project is documented the way a laboratory documents its work: the question it answers, why it matters, how it is studied, what it is finding and where it leads next.

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3 of 6 projects

Project 02

Active · Honours research

Negotiating Trust in Artificial Intelligence

An ethnographic study of how people arrive at — and withdraw — trust in AI-mediated services, treating trust as an ongoing negotiation with institutions rather than an attitude towards a tool.

How do people negotiate trust in artificial intelligence when they cannot inspect, contest or fully understand the system deciding on their behalf?

Why it matters: Adoption strategies routinely assume that better performance produces confidence. In practice people calibrate trust against institutional history, perceived recourse and the conduct of the person in front of them.
Methods: Semi-structured interviewsParticipant observationThematic and narrative analysisReflexive field notes
Findings: Emerging: trust attaches to accountability, not accuracy. Participants ask who answers for the outcome long before they ask how the system works.
Impact: Gives institutions a language for trust that survives contact with real users, and a way to diagnose failures that no performance metric surfaces.
Future implications: Extension into a comparative Masters study across health, education and financial services.

Project 03

In development

AI and Healthcare Decision-Making

How clinical decisions are reshaped when predictive and decision-support systems enter consultation rooms, wards and referral pathways.

When machine recommendation meets clinical judgement, whose knowledge prevails — and what happens to responsibility?

Why it matters: Health systems are among the fastest adopters of AI and among the least forgiving contexts for unexamined assumptions about behaviour.
Methods: Clinical ethnographyPractitioner interviewsDecision-pathway mappingDocument and protocol analysis
Findings: Planned. Early scoping suggests overrides are rarely about disagreement with the output and often about protecting a relationship with the patient.
Impact: Practical guidance for deploying decision support without displacing the moral work of care.
Future implications: Collaboration with health services on implementation and evaluation design.

Project 05

Ongoing enquiry

Human Behaviour in AI Systems

A cross-cutting enquiry into the adaptations, workarounds and quiet refusals through which people absorb AI systems into everyday routines.

How do people adapt their behaviour to AI systems, and what does that adaptation cost them?

Why it matters: Workarounds are usually recorded as non-compliance. Read carefully, they are the most precise available description of a design failure.
Methods: Observational studyInteraction analysisComparative case studies
Findings: Emerging: users perform legibility for systems — reshaping how they write, answer and behave so a machine will read them correctly.
Impact: A behavioural vocabulary product and service teams can design against.
Future implications: Development into a framework for human-centred AI evaluation.