Alongside my empirical research, I work on how research gets done and how it gets used. Two threads run through it: the practice of doing rigorous empirical work now that AI is in the workflow, and the science-policy interface, where evidence from the social sciences has to be brokered into decisions within existing systems. This page lists some of the talks, essays and public writing that come out of that work.
TALKS
AI Is Not an Algorithm: What That Means for Research Practice, Machine Collaborators seminar series, 9 July 2026. [Slides (PDF)]
Generative AI is not deterministic, so supervising it is not code review. I set out a consistency-creativity dial for choosing how much latitude to give AI on a given task, work through failures at each end drawn from my own projects, and argue that the interesting territory is the middle: how to get AI's leverage without compiling away what makes the work yours. The organizing question is not whether to adopt or resist, but what you keep human, and why.
WRITING
The One-Voice Machine (forthcoming, 2026), INGSA-Europe, Science Advice in Challenging Settings series, with Frontiers Policy Labs.
AI-assisted evidence synthesis can manufacture a single voice that a field does not actually have. When the social sciences genuinely disagree about methods and standards of evidence, a synthesis that smooths this away produces a consensus that is brittle, and once contested, the damage lands on the adviser's legitimacy rather than on the policy brief.
On AI and research work styles (in progress)
A longer essay on why most AI productivity advice is written for one kind of worker, and what changes when it is not.