---
title: "AI Judges Proposed to Enhance Prediction Market Efficiency"
type: "News"
locale: "en"
url: "https://longbridge.com/en/news/273587762.md"
description: "AI judges are proposed to enhance the efficiency of prediction markets by addressing challenges in determining actual outcomes. These challenges often arise in smaller events due to opaque settlement mechanisms that undermine trust and accuracy. Experts suggest using large language models (LLMs) as adjudicators to improve transparency and fairness. This includes on-chain rule commitments and tamper-resistant models. Developers are encouraged to standardize practices and build transparency tools to enhance market operations."
datetime: "2026-01-24T14:23:34.000Z"
locales:
  - [zh-CN](https://longbridge.com/zh-CN/news/273587762.md)
  - [en](https://longbridge.com/en/news/273587762.md)
  - [zh-HK](https://longbridge.com/zh-HK/news/273587762.md)
---

> Supported Languages: [简体中文](https://longbridge.com/zh-CN/news/273587762.md) | [繁體中文](https://longbridge.com/zh-HK/news/273587762.md)


# AI Judges Proposed to Enhance Prediction Market Efficiency

Prediction markets face significant challenges, not in pricing future events, but in determining actual outcomes, according to PANews. These issues frequently arise in smaller events, where incorrect or opaque settlement mechanisms can undermine market trust, liquidity, and price signal accuracy. The introduction of AI judgment systems is suggested to improve settlement efficiency and scalability while ensuring transparency and fairness. Industry experts recommend using large language models (LLMs) as adjudicators in prediction markets. This approach includes on-chain rule commitments, manipulation resistance, enhanced transparency, and increased neutrality. For instance, during contract creation, specific LLM models, timestamps, and judgment prompts can be encrypted and recorded on the blockchain, allowing traders to understand the complete decision-making process in advance. Fixed model weights reduce the risk of tampering, while open and auditable settlement mechanisms prevent arbitrary human rulings. Developers are encouraged to experiment with low-risk contracts, standardize best practices, build transparency tools, and engage in ongoing meta-level governance to further improve prediction market operations.

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