Justice through AI: How to Automate Judgments in Prediction Markets

Prediction markets have long awaited a revolution. According to experts, fairness and accuracy in determining event outcomes are the core elements that can dismantle the entire trust mechanism. Most problems arise not during price formation but specifically at the contract settlement stage, especially when dealing with smaller or more complex events where human judgment becomes a bottleneck. PANews analyzed how insufficient transparency and inconsistent processes cause market participants to doubt its fairness, leading to liquidity leaks and distorted price signals.

Why Traditional Settlement Methods Are Unsatisfactory

Modern prediction markets face a fundamental problem: human judges can make subjective decisions, be influenced by interests, or simply make mistakes. With a small number of contracts, these errors can have catastrophic consequences for the fairness of the entire system. A settlement mechanism lacking transparency breeds distrust and reduces traders’ willingness to take risks. Liquidity dries up, prices become less informative, and the market ceases to perform its primary function.

Blockchain and LLMs: A Recipe for Transparency and Fairness

The proposed solution involves using large language models (LLMs) as objective arbiters embedded within smart contract mechanisms. This approach combines several advantages: predictability (all rules are pre-recorded on the blockchain), resistance to manipulation (frozen model weights), complete transparency of the judgment process, and guaranteed fairness through the elimination of human subjectivity.

In practice, this looks like: when deploying a contract, a specific LLM, the query time, the description of the judgment, and other parameters are encrypted and recorded on the blockchain. Participants can analyze the entire decision chain before placing their funds. Since model weights cannot be altered, the risk of tampering drops to zero. Audited settlement mechanisms exclude arbitrary or self-interest-driven human decisions.

Practical Steps for Deploying Fair Judgment Systems

Experts call for developers to gradually implement these systems: starting with experimentation on low-risk contracts, standardizing best practices, developing tools for monitoring transparency, and continuously improving system management at the meta-level. The goal is to create an ecosystem where fairness does not depend on trust in individual persons but is guaranteed by the technological architecture.