Why did you choose your MSc and Edinburgh Futures Institute?
Before EFI, I studied Internet and New Media. My interest in inequality grew from media research, especially questions about the digital divide, unequal digital skills, and differences in who becomes visible online.
I chose Data, Inequality and Society because I wanted to move from studying media mainly as communication to studying data and digital systems as social structures. I became interested in how platforms and algorithms distribute attention and opportunity. Recommendation systems, for example, can make some people, voices, or content increasingly visible while others become harder to find. That raised wider questions for me about how data systems can reproduce or deepen existing inequalities. The programme gave me a way to connect my media background with a sociological approach to inequality.
Also, Edinburgh is a city that supports both quiet study and an active social life, with societies, talks, cultural events, and arts festivals every August.
How did you get to where you are?
After EFI, I kept following how digital systems shape social outcomes, and my attention gradually moved toward generative AI. I now work independently on questions around AI-generated information.
One part of my work examines how generative models represent social concepts. I have carried out exploratory audits of visual models, studying recurring representations of gender, occupation, religion, and relationships. I also look at how much prompting is needed to move a model away from recurring patterns.
I am also interested in how AI affects human judgement. Recently, I have been learning about performative prediction and preference learning. AI systems can influence the preferences and behaviours that later become part of their feedback and training environment.
Another part of my work focuses on institutional response. I have been working through a California policy scenario involving harmful or misleading AI-generated information, including which public bodies or officials may need to respond, what signals should attract attention, and how a concern can become actionable.
EFI gave me the habit of connecting technical evidence, human behaviour, and institutional responsibility. That way of thinking still guides my research.
What did you gain from your time at Edinburgh Futures Institute / the University of Edinburgh?
EFI changed the way I approach a problem.
In Interdisciplinary Futures, I worked with people from very different disciplinary backgrounds. Their questions often exposed assumptions that felt obvious within my own field. I became more comfortable explaining my reasoning clearly and testing ideas from several perspectives.
Data Science for Society and Representing Data strengthened the way I make an argument. I became more likely to ask what evidence could support a claim, what could be measured, and where the limits of the evidence were. Ethical Data Futures and Data Civics added questions about responsibility, affected groups, and institutional power.
Some of that learning happened outside the classroom. In Data Civics, we used Edinburgh itself as a research setting and went out to conduct local fieldwork and speak with residents. That made questions about data, place, and civic life much more concrete for me.
These habits still shape my work. I usually start with an observable problem, identify the evidence, and then consider who is affected and who has the ability to respond.
Tell us about your Futures Project
For my Futures Project, titled “Gender Inequality on YouTube: A Comparative Study of Commentary on Male and Female Vloggers,” I studied gender differences in audience responses to male and female vloggers on YouTube. I compared comments on a male-led vlogging channel, The Try Guys, with comments on a female vlogger, Safiya Nygaard, and analysed around 1,200 comments using computational text analysis, thematic analysis, and qualitative coding.
The comments showed clear differences in what audiences noticed. Appearance featured much more strongly in comments about the female vlogger, while comments about the male vloggers focused more on video content, personality, and performance. I also found cases where evaluations of Safiya’s abilities were linked to her appearance.
The project taught me how to turn gender inequality into observable categories and how to connect computational patterns with qualitative interpretation. This now links directly to my work on generative AI. I used to study how people construct gender through comments. I now study how AI systems construct social concepts through generated images.
My advice to students is to use coursework to build research experience and make the Futures Project empirical whenever possible. Collect data, test a question, and examine how the technology itself behaves.
Do you have any highlights or a favourite memory of your time at the University?
One course I still think about is Migration and Forced Displacement in a Digital Age. For my policy paper, I examined in-home surveillance of migrant domestic workers, particularly Filipino workers in Hong Kong. I focused on how CCTV, employer monitoring, and migration rules shape privacy, behaviour, and workers’ ability to challenge surveillance.
I still follow this field because similar questions appear in newer technologies. In Finland, research has examined digital asylum procedures. On the Thailand-Myanmar border, research on biometric digital identity has raised issues around consent and power asymmetry. This way of thinking about technology, institutional power, and vulnerable groups still shapes how I approach AI and governance today.
I also have warm memories of EFI’s staff and activities. I found the staff friendly and approachable, and the EFI end-of-year ball happened to fall on my birthday. I spent the evening dancing with classmates, and that night remains one of my clearest memories of the year.
What advice would you give to someone considering studying at Edinburgh Futures Institute?
I would advise students to use EFI as a research network as well as a degree programme.
EFI is interdisciplinary and gives you access to people working across data, social science, design, policy, humanities, and computing. Join research events, speak to researchers outside your programme, and follow the research clusters that match the questions you care about. This is what I wish I’d known before I joined EFI.
EFI has several research clusters, including Critical Data Studies, Digital Economy and Society, Media and Communications, and Creativity, AI, and the Human. If the work of external partners across government, public services, business, and the wider community connects with your research topic or career direction, speak with them and look for opportunities to contribute. EFI formally supports student projects involving industry, government, and community partners.
