Luke Hewitt

Luke Hewitt

I work on computational & experimental tools for measuring what changes people’s beliefs/attitudes, with application to effective advocacy, public health communication, AI safety, and social science methodology. My research combines RCTs, LLMs, expert forecasting and hierarchical Bayesian models.

Currently:

Previously:

  • AI safety consulting, OpenAI (GPT-4o persuasion evaluation)
  • Research data scientist, Swayable (persuasion measurement, expt. design & analysis)
  • PhD in AI / Cognitive Science, MIT
  • MEng in Mathematical Computation, UCL
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Research

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

How will advanced AI systems impact democracy? Summerfield et al. (in review)

Leveraging Large Language Models to Predict Results of Experiments in the Social Sciences Hewitt*, Ashokkumar* et al. (in review)

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)