I'm Luke Thorburn , a PhD student in Safe & Trusted AI at King's College London.
My core research interests lie at the intersection: algorithms ∩ conflict. My background is in probability and statistics.
You can email me at .
Updates
Infrequent: email newsletter. Frequent: Mastodon, Bluesky.
Curated
Newest
Microblog
Other People
Given you're here, you might also be interested in the work of — in no particular order — Kristen Altenburger, Jay Baxter, Paul Bouchaud, Jason Burton, Micah Carroll, Tom Cunningham, Fernando Diaz, Xin Dong, Evelyn Douek, Tarleton Gillespie, Paul Gölz, Julie Hawke, Colin Irwin, Ravi Iyer, Tian Jin, Andrew Konya, David Krueger, Seth Lazar, Christian List, Colin Megill, Smitha Milli, Arvind Naranayan, Puja Ohlhaver, Aviv Ovadya, Helena Puig Larrauri, Manon Revel, Edward Saperia, Lisa Schirch, Nathan Schneider, Elizabeth Seger, Jonathan Stray, Zachary Wojtowicz, Nathan Young, Amy Zhang, Emillie de Keulenaar, and .
Understanding Recommenders
I write a blog about recommender systems with Pri Bengani and Jonathan Stray. We try to make it rigorous and accessible. Lots of people seem to like it.
Collaboration + Opportunities
Increasingly, I have more worthy research opportunities open to me than I have the bandwidth for, often with funding attached. If you are interested in collaborating or having offers of work passed in your direction, please get in touch!
Reference Works
All
Shaping the Future of Social Media with Middleware ↗
Toward Democracy Levels for AI ↗
Views on AI aren't binary — they're plural ↗
The Moral Case for Using Language Model Agents for Recommendation ↗
Societal Sorting as a Systemic Risk of Recommenders ↗
Views on AI aren't binary — they're plural ↗
Places to publish
Underexplored Ways to Regulate Social Media ↗
Mapping the space of social media regulation ↗
Experiments are the Best Kind of Transparency ↗
Research Agenda for Sociotechnical Approaches to AI Safety ↗
Recommender Systems Very Different From Ours
How to redesign social media algorithms to bridge divides ↗
Bridging Systems ↗
What is Media Diversity and Do Recommender Systems Have It? ↗
What's the Difference Between Search and Recommendation? ↗
SHAPE: A Framework for Evaluating the Ethicality of Influence ↗
Error in the Euclidean Preference Model ↗
Submission to Australian Government ↗
Evaluating recommender systems in relation to the dissemination of illegal and harmful content in the UK ↗
Polarization as Probabilistic Dependence ↗
Error in the Euclidean Preference Model ↗
Making Amplification Measurable ↗
When You Hear “Filter Bubble”, “Echo Chamber”, or “Rabbit Hole” — Think “Feedback Loop” ↗
Submission to UNESCO ↗
Is Optimizing for Engagement Changing Us? ↗
Optimizing Language Models for Argumentative Reasoning ↗
Algorithmic Risks to Collective Decision-Making ↗
A Menu of Recommender Transparency Options ↗
How to Measure the Effects of Recommenders ↗
Submission to International Observatory on Information and Democracy ↗
What Will “Amplification” Mean in Court? ↗
The IC Rating Scale as a Measure of Analytic Rigor ↗
What's Right and What's Wrong with Optimizing for Engagement ↗
What Does it Mean to Give Someone What They Want? ↗
Using argument mapping to improve clarity and rigour in written intelligence products ↗
How Platform Recommenders Work ↗
The Impact of Evidence on Decision Making ↗
Towards Argument Embeddings ↗
Analytic Rigour in Intelligence ↗
Charting our way back to normal ↗
Enhancing human analysis of large corpora via argument mining and backlinks—a prototype ↗
Discourse-tionary
Classification of failure modes in public discourse
Disagreement is asymmetric
Tools to Ease the Choice and Design of Protein Crystallisation Experiments ↗
Metaphors for public discourse
Fractional Brownian motion
Using Anki for mathematics
What is a probability?
What's good is bad
License + Disclaimer
This site is licensed under the Creative Commons public domain (CC-0) license, but attribution is appreciated. Views expressed are my own, and probably don't coincide with those of any entity with which I am affiliated.