I’m a computational cognitive scientist researching persuasion and influence. Most recently I've worked on developing experiments to evaluate AI persuasiveness, for OpenAI, UK AISI, and at Transluce. For my PhD I studied political persuasion at MIT, then was a research fellow at Stanford evaluating the capability of AI to simulate human attitudes.

I’m currently organizing the first Workshop on AI, Manipulation and Information Integrity at IASEAI 2026. Submit an abstract by Jan 10!

Research highlights: Using AI to simulate human experiments; Measuring persuasion in frontier AI models; Measuring the impact of political ad-testing

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Research

→ The levers of political persuasion with conversational AI Hackenburg et al. (Science, 2025)

→ How will advanced AI systems impact democracy? Summerfield et al. (Nature Human Behavior, 2025)

→ Outcome-based Reinforcement Learning to Predict the Future Turtel et al. (TMLR, 2025)

→ Predicting results of social science experiments using large language models Hewitt*, Ashokkumar* et al. (in review)

→ DeliberationBench: A normative benchmark for the influence of LLMs on users’ views Hewitt et al. (in review)

→ Encouraging vaccination using the creativity and wisdom of crowds Tappin et al. (in review)

→ Large language models are more persuasive than incentivized human persuaders Schoenegger et al. (in review)

→ The impact of AI message-testing on public discourse Hewitt (IASEAI 2025)

→ Quantifying the returns to persuasive message-targeting using a large archive of campaigns’ own experiments* - Tappin, Hewitt, Coppock (APSA, 2024)

→ GPT-4o System Card: Persuasion OpenAI (2024)

→ How experiments help campaigns persuade voters: evidence from a large archive of campaigns’ own experiments Hewitt et al. (APSR, 2024)

→ Using survey experiment pre-testing to support future pandemic response Tappin and Hewitt (PNAS Nexus, 2024)

→ Listening with generative models Cusimano et al. (Cognition, 2024)

→ Quantifying the persuasive returns to political microtargeting Tappin et al. (PNAS, 2023)

→ Emotion prediction as computation over a generative Theory of Mind Houlihan et al. (Phil. Trans. A, 2023)

→ DreamCoder: growing generalizable, interpretable knowledge with wake-sleep bayesian program learning Ellis et al. (Phil. Trans. A, 2023)

→ Rank-heterogeneous effects of political messages: Evidence from randomized survey experiments testing 59 video treatments Hewitt et al. (working paper)

→ Hybrid memoised wake-sleep: Approximate inference at the discrete-continuous interface Le et al. (ICLR, 2022)

→ DreamCoder: Bootstrapping Inductive Program Synthesis with Wake-Sleep Library Learning Ellis et al. (PLDI, 2021)

→ Estimating the Persistence of Party Cue Influence in a Panel Survey Experiment Tappin et al. (JEPS, 2021)

→ Learning to learn generative programs with memoised wake-sleep Hewitt et al. (UAI, 2020)

→ Inferring structured visual concepts from minimal data Qian et al. (CogSci, 2019)

→ Learning to infer program sketches Nye et al. (ICML, 2019)

→ The Variational Homoencoder: Learning to learn high capacity generative models from few examples Hewitt et al. (UAI, 2018)

→ Auditory scene analysis as Bayesian inference in sound source models Cusimano et al. (CogSci, 2017)

CV

• Research Fellow, Transluce
• AI safety consulting, OpenAI (GPT-4o persuasion evaluation)
• AI safety consulting, UK AI Security Institute (evaluating AI persuasion capabilities)
• Senior Research Fellow, Stanford (using AI to simulate human experiments)
• Co-founder, Rhetorical Labs; Fellow, Future of Life Foundation; Member, South Park Commons
• Research data scientist, Swayable (persuasion measurement & national opinion polling)
• PhD in Computational Cognitive Science, MIT; MEng in Mathematical Computation, UCL