# Artificially Intelligent Opinion Polling

arXiv: https://arxiv.org/abs/2309.06029 · HTML: https://mlsanity.com/paper/2309.06029/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2023.

## Built on by (most cited first, 3 of 3)

- [How will advanced AI systems impact democracy?](https://mlsanity.com/paper/2409.06729/) (2024)
- [Characterizing the ability of LLMs to recapitulate Americans' distributional responses to public opinion polling questions across political issues](https://mlsanity.com/paper/2603.20229/) (2026)
- [Leveraging the Power of AI and Social Interactions to Restore Trust in Public Polls](https://mlsanity.com/paper/2511.07593/) (2025)

## Abstract (arXiv)

We seek to democratise public-opinion research by providing practitioners with a general methodology to make representative inference from cheap, high-frequency, highly unrepresentative samples. We focus specifically on samples which are readily available in moderate sizes. To this end, we provide two major contributions: 1) we introduce a general sample-selection process which we name online selection, and show it is a special-case of selection on the dependent variable. We improve MrP for severely biased samples by introducing a bias-correction term in the style of King and Zeng to the logistic-regression framework. We show this bias-corrected model outperforms traditional MrP under online selection, and achieves performance similar to random-sampling in a vast array of scenarios; 2) we present a protocol to use Large Language Models (LLMs) to extract structured, survey-like data from social-media. We provide a prompt-style that can be easily adapted to a variety of survey designs. We show that LLMs agree with human raters with respect to the demographic, socio-economic and political characteristics of these online users. The end-to-end implementation takes unrepresentative, unsrtuctured social media data as inputs, and produces timely high-quality area-level estimates as outputs. This is Artificially Intelligent Opinion Polling. We show that our AI polling estimates of the 2020 election are highly accurate, on-par with estimates produced by state-level polling aggregators such as FiveThirtyEight, or from MrP models fit to extremely expensive high-quality samples.