The system allows users to train an AI agent on their device with their personal preferences. These preferences are then compressed using a Bert Large Language Model (LLM) into an embedding, which is sent to a semi-trusted server tasked with executing automated votes. For each new poll, the server calculates the user's vote using a straightforward ML model and produces a Zero-Knowledge Machine Learning (ZKML) attestation to verify the vote's correctness. This attestation encrypts the vote for publication on the blockchain. Users have the option to decrypt and review their vote; if dissatisfied with the automated choice, they can manually revote.
This project addresses the fundamental issue of low voter participation, which often stems from people's reluctance to make decisions. By creating a system that autonomously makes decisions on behalf of the user—in a manner consistent with their preferences—we aimed to increase participation by simplifying the voting process.
