Luke Hewitt

I’m a computational cognitive scientist researching persuasion and influence. Most recently I’ve worked on evaluating AI persuasion for OpenAI1, UK AISI2, and Transluce. For my PhD I studied generative models3 and political persuasion4 at MIT, then worked on simulating human experiments5 at Stanford.

Research

Recent highlights: Predicting results of social science experiments using large language models; The levers of political persuasion with conversational AI; How experiments help campaigns persuade voters

→ AI systems out-persuade expert humans

Hackenburg et al. (in review)

→ The AI Epistemic Deference Index: A Continuous Measure of Sycophancy

Botas et al. (in review)

→ AI Epistemic Risks: Emerging Mechanisms & Evidence

Yang et al. (in review)

→ Artificial intelligence can persuade people to take political actions

Hewitt*, Hackenburg* et al. (in review)

→ DeliberationBench: A normative benchmark for the influence of LLMs on users’ views

Hewitt et al. (IASEAI, 2026)

→ 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)

→ 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