LLMs and Prediction Markets Reveal Systematic Bias in News-Based Strategic Forecasts
A new arXiv preprint demonstrates that large language models (LLMs), when paired with prediction market data, can quantify how information sources bias strategic predictions. Analyzing 111 Ukraine-related prediction markets, the study finds that English news context systematically biases territorial forecasts, with predictions favoring territorial capture being incorrect 64–72% of the time. The analysis suggests that this bias originates mainly from the text sources rather than the LLMs themselves, and persists across multiple model architectures.
Why it matters: The findings highlight that AI systems relying on real-world text sources can inherit and propagate significant biases, which could impact strategic decision-making in high-stakes domains.
Full story at: arXiv Computation and Language ↗