Quantifying the Political Prism: A Framework for Error-Aware AI Governance Forecasting
Igor Mizin · Team PAIR (POLITICAL AI RESEARCHERS)
Submitted to The AI Forecasting Hackathon. Sprint projects are early-stage work by participants, not Apart Research publications.
Our project conducts a rigorous meta-forecasting analysis of Large Language Models (LLMs) themselves, identifying them as a significant source of systematic error in predicting political and socio-economic outcomes.
We empirically map a critical source of uncertainty and bias — ingrained ideological skew — that is currently absent from most AI timeline and impact models. By benchmarking six leading LLMs against expert political science consensus, we quantify how this bias leads to:
Asymmetric errors in economic forecasts (e.g., consistently underestimating GDP growth under conservative policies).
Unreliable assessments of political regimes and their stability.
A false sense of objectivity in AI-generated policy analysis.
This work provides a systematic critique of the LLM-based forecasting methodology, highlighting its limitations and creating a foundational educational resource for ensuring rigorous, bias-aware forecasting practices in AI governance and policy circles.
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Cite this project
@misc{mizin2025quantifying,
title = {{Quantifying the Political Prism: A Framework for Error-Aware AI Governance Forecasting}},
author = {Igor Mizin},
year = {2025},
month = nov,
note = {Submitted to The AI Forecasting Hackathon, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/quantifying-the-political-prism-a-framework-for-erroraware-ai-governance-forecasting-xtau}},
url = {https://apartresearch.com/sprints/projects/quantifying-the-political-prism-a-framework-for-erroraware-ai-governance-forecasting-xtau}
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