Профессор Стэнфорда: Искусственный интеллект как решение для справедливых рынков предсказаний Translation: Professor from Stanford: Artificial Intelligence as a Solution for Fair Prediction Markets

Artificial intelligence has the potential to serve as an integrated judge within blockchain-based prediction markets. This viewpoint was expressed by Andrew Hall, a professor of political economy at Stanford University’s Graduate School of Business.

He illustrated the challenge of «fair» dispute resolution using the example of the Venezuelan presidential elections.

Last year, contracts worth over $6 million were made regarding the outcome of the elections. However, after the campaign, the market found itself in confusion:

“Should the outcome of contracts in prediction markets align with the ‘official’ information (Maduro’s victory) or the ‘consensus of reliable reports’ (opposition’s victory)?” Hall questioned.

This is not an isolated incident, the expert pointed out. In another case, there were allegations of manipulation regarding Ukraine’s territorial dispute.

Hall emphasizes the importance of establishing a fair contract resolution system that garners public trust. In such a scenario, prices would serve as significant signals for society.

Similar issues also plague financial markets. For years, the International Swaps and Derivatives Association has been grappling with regulatory challenges in the credit default swap market—contracts that pay out in the event of a company or country’s bankruptcy.

Decision-making committees vote on whether credit events have occurred. However, the process faces criticism for its lack of transparency, potential conflicts of interest, and inconsistent outcomes.

«The fundamental problem remains the same: when large sums are contingent on determining what occurred in an ambiguous situation, any resolution mechanism becomes a target for manipulation, and ambiguity becomes a potential point of contention,» Hall stated.

He outlined several essential characteristics that any viable solution should possess:

Committees made up of people can meet some of these characteristics, but they are susceptible to manipulation and cannot maintain neutrality.

Hall proposes the use of large language models (LLMs) as judges, with each model and prompt recorded on the blockchain at the time of contract creation.

The basic architecture looks like this:

This approach addresses several key issues:

However, there are drawbacks: AI can make mistakes. It might misinterpret a news article or fabricate a fact.

While manipulation is not impossible, it becomes more challenging to execute. Fraudsters could commission the dissemination of particular information in major media outlets. This is expensive but feasible.

There is also the risk of attacks on LLM training data, but this requires actions to be taken well ahead of contract initiation.

The AI-based solution replaces one set of problems with another that is more manageable. Platforms should experiment with different LLMs for practical experience, according to Hall.

As best practices emerge, the community needs to work on standardizing combinations of AI programs. This will help concentrate liquidity, the author believes.

It is worth noting that in January, analysts from a16z crypto predicted a rise in prediction markets and zero-knowledge (ZK) proofs.