Setting Up a Token-Based Voting System for AI Decisions

Token-based governance is the standard mechanism for decentralized decision-making in Web3, allowing stakeholders to vote proportionally to their holdings. For AI systems, this model can be applied to critical decisions such as selecting training datasets, adjusting model parameters (like temperature or top_p), approving funding for compute resources, or upgrading the underlying smart contract logic. Unlike centralized AI development, a well-designed on-chain system ensures transparency, auditability, and resistance to unilateral control by any single entity. Projects like Ocean Protocol for data markets and Bittensor for decentralized machine learning have pioneered early models of this approach.

The core architecture involves a smart contract that manages proposal creation, voting, and execution. A typical AIGovernance contract would include functions to: createProposal(bytes memory _calldata, string memory _description), castVote(uint256 _proposalId, bool _support), and executeProposal(uint256 _proposalId). Voting power is usually calculated as a snapshot of a user's token balance at a specific block number to prevent manipulation. For AI-specific decisions, the _calldata might encode a call to a separate Model Registry contract to update a parameter, or to a Treasury contract to release funds to a researcher's address.

Here is a simplified example of a vote casting function using OpenZeppelin's governance contracts, which many projects fork and extend:

solidityCopy
function castVote(uint256 proposalId, uint8 support) public virtual override returns (uint256) {
    address voter = _msgSender();
    uint256 weight = getVotes(voter, proposalSnapshot(proposalId));
    require(weight > 0, "GovernorVotingSimple: vote not allowed");
    _castVote(proposalId, voter, support, weight, "");
    return weight;
}

The getVotes function retrieves the voter's token balance from the historical snapshot. This pattern ensures the vote reflects the stakeholder's economic commitment at the time the proposal was made, not at the time of voting.

Key design considerations for an AI governance system include voting delay (time between proposal submission and voting start), voting period (duration of the vote), and quorum requirements (minimum participation threshold). For high-stakes AI decisions, such as changing a model's safety filter, you might implement a longer voting period (e.g., 7 days) and a high quorum (e.g., 20% of circulating supply). It's also critical to have a timelock contract that queues a successful proposal for execution after a delay, giving users a final window to exit the system if they disagree with the impending change.

Beyond basic yes/no voting, more sophisticated mechanisms can be implemented. Quadratic voting, where the cost of votes scales quadratically with the number cast, can mitigate whale dominance and better reflect the intensity of preference. Conviction voting, used by projects like 1Hive, allows votes to accumulate weight over time, suitable for ongoing funding decisions for AI model maintenance. The choice of mechanism depends on whether you prioritize speed, resistance to coercion, or accurate preference aggregation for complex AI governance topics.

Finally, the frontend and tooling layer is essential for user participation. This involves indexing on-chain proposal events with The Graph, displaying clear vote histories, and integrating wallets like MetaMask for signature-based voting (e.g., via EIP-712). For maximum security and decentralization, the entire lifecycle—from proposal submission to execution—should be permissionless and verifiable on-chain. This creates a robust framework where an AI's evolution is directly guided by its aligned stakeholder community.

Before writing any code, you need a development environment and a clear understanding of the core components. You will need Node.js (v18 or later) and npm or yarn installed. For smart contract development, we recommend using the Hardhat framework, which provides a local Ethereum network, testing utilities, and deployment scripts. Alternatively, Foundry is a popular choice for its speed and direct Solidity testing. You'll also need a basic understanding of Solidity for the voting contract and JavaScript/TypeScript for the frontend integration and tests.

The voting system's logic will be encapsulated in a smart contract. At a minimum, this contract must manage a list of proposals, track votes cast by token holders, and enforce voting rules like quorum and duration. You will need to decide on a token standard for governance; ERC-20 is the most common, but ERC-721 (NFTs) can be used for non-fungible voting power. For on-chain execution of passed proposals, consider integrating with a multisig wallet or a timelock controller (like OpenZeppelin's) to add a security delay. All code examples will use Solidity ^0.8.20.

Start by initializing a new Hardhat project: run npx hardhat init and select the TypeScript template. Install essential dependencies: npm install @openzeppelin/contracts dotenv. OpenZeppelin Contracts provide audited, standard implementations for tokens and security utilities. Create a .env file to store sensitive data like private keys and RPC URLs (e.g., for Sepolia testnet: ALCHEMY_SEPOLIA_URL). Fund your test wallet with Sepolia ETH from a faucet.

The first contract you'll write is the governance token. Using OpenZeppelin's ERC-20, you can create a simple, secure token with minting capabilities. This token will represent voting power. Next, you will write the main governance contract. It should import the token, define a Proposal struct with fields for id, description, voteCount, and executed, and maintain a mapping of which addresses have voted on which proposals. Implement functions to createProposal, vote, and executeProposal.

A critical security pattern is the use of a timelock. This contract sits between the governance contract and the target contract (e.g., a treasury). When a proposal passes, it is queued in the timelock and can only be executed after a minimum delay, giving token holders time to react to malicious proposals. Implement checks in your executeProposal function to ensure only the timelock (or a designated executor) can call it. This separation of powers is a best practice for production systems.

Finally, plan your testing strategy. Write comprehensive Hardhat tests that simulate the full governance lifecycle: minting tokens, delegating votes, creating proposals, voting, and executing. Test edge cases like double-voting, voting after a deadline, and executing a failed proposal. Use hardhat console.log for debugging. Once tested locally, you can deploy to a testnet like Sepolia using a script, verifying your contract on a block explorer like Etherscan to allow for public interaction and verification.

Token-based voting is a foundational mechanism for decentralized governance, allowing a community of stakeholders to make collective decisions. In the context of AI, this can govern critical parameters like model training data sources, deployment parameters, upgrade paths, and treasury allocations. The system's security and fairness are anchored in the underlying blockchain, which provides cryptographic verification of votes and immutable record-keeping. This creates a transparent audit trail for all governance actions, a crucial feature for high-stakes AI decisions.

The core technical architecture involves several smart contracts. A governance token contract (often an ERC-20 or ERC-1155) defines the voting power. A governor contract (like OpenZeppelin's Governor) manages the proposal lifecycle: creation, voting, and execution. Proposals are executable code that can call functions on a target contract, such as an AI model registry or treasury. For example, a proposal could call updateModelParameters() on a target contract to adjust an AI agent's operational constraints after a successful vote.

Implementing a basic proposal starts with the governor contract. A user with sufficient token balance creates a proposal by specifying the target contract address, the function to call, and the encoded calldata. The community then votes during a defined period, with weight typically determined by token balance (e.g., one token equals one vote). A common standard is Compound's Governor Bravo pattern, which uses a timelock contract to queue successful proposals, adding a security delay before execution. Here's a simplified snippet for creating a proposal using OpenZeppelin:

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function propose(
    address[] memory targets,
    uint256[] memory values,
    bytes[] memory calldatas,
    string memory description
) public returns (uint256 proposalId);

Voting mechanisms must be carefully designed to prevent manipulation. Snapshot voting is a popular gasless alternative where votes are signed off-chain and aggregated, reducing voter cost but relying on a trusted oracle. For on-chain voting, consider mechanisms like vote delegation (as used by Uniswap) to improve participation, and quorum requirements to ensure sufficient stakeholder engagement. Critical decisions for AI systems may also employ quadratic voting to reduce whale dominance or futarchy (prediction market-based governance) to tie outcomes to measurable performance metrics.

Integrating voting with AI operations requires a clear on-chain interface. The target contract that the governor controls could be an AI model registry that stores model hashes and version metadata, or a parameter controller that adjusts inference settings. For instance, after a vote passes, the governor executes a transaction that calls setInferenceCost(0.001 ETH) on the controller contract. This creates a verifiable link between community sentiment and the AI's operational behavior, enabling decentralized oversight over potentially impactful systems.

When deploying, audit all contracts thoroughly, especially the permissioning logic linking the governor to target contracts. Use established frameworks like OpenZeppelin Governor for battle-tested base code. Set realistic proposal and voting periods (e.g., 3-7 days) to balance agility with deliberation. Ultimately, a well-designed token-based voting system transforms AI development from a centralized process into a transparent, community-aligned endeavor, distributing control and accountability across its stakeholder base.

An ERC-20 governance token is a fungible digital asset that grants its holders the right to vote on proposals. Unlike a standard utility token, its primary function is to measure and delegate decision-making power within a decentralized autonomous organization (DAO) or protocol. For an AI system, this could mean voting on model upgrades, parameter adjustments, treasury allocations, or ethical guidelines. The token's distribution—whether via airdrop, sale, or contribution rewards—defines the initial stakeholder landscape.

The most secure and efficient method is to use a battle-tested library like OpenZeppelin Contracts. Start by installing the library: npm install @openzeppelin/contracts. You'll inherit from the ERC20 and ERC20Permit contracts. ERC20 provides the core token functionality, while ERC20Permit enables gas-less token approvals via signatures, a crucial feature for improving voter experience in governance interfaces. Avoid writing a token contract from scratch to mitigate security risks.

Your contract should mint an initial supply to a designated address (e.g., a multisig treasury) during deployment. Use the constructor to set the token's name, symbol, and initial allocation. A typical deployment script using Hardhat or Foundry would look like this:

solidityCopy
// SPDX-License-Identifier: MIT
pragma solidity ^0.8.20;
import "@openzeppelin/contracts/token/ERC20/ERC20.sol";
import "@openzeppelin/contracts/token/ERC20/extensions/ERC20Permit.sol";
contract GovernanceToken is ERC20, ERC20Permit {
    constructor(address initialHolder)
        ERC20("AI Governance Token", "AIGOV")
        ERC20Permit("AI Governance Token")
    {
        _mint(initialHolder, 1000000 * 10 ** decimals());
    }
}

After deploying to a testnet (like Sepolia or Goerli), verify the contract source code on a block explorer like Etherscan.

Before connecting the token to a governance module like OpenZeppelin Governor, you must delegate voting power. In many token implementations, holders must actively delegate their votes to themselves or another address to activate their voting power. You can build this logic into your token's constructor or create an initial delegation transaction. For the AI system, consider if voting power should be delegatable to experts or must remain with the token holder, as this impacts the decision-making structure.

Finally, consider the token's economic and security properties. Will it have a minting cap? Is there a mechanism for future token distribution? For a governance system controlling AI, you might implement a timelock on large token transfers or a vesting schedule for team allocations to prevent sudden shifts in voting power. Document these decisions clearly, as they form the constitutional layer of your on-chain governance system. The next step is to deploy a Governor contract that uses this token as its voting asset.

An on-chain voting contract formalizes governance by encoding proposal creation, voting, and execution logic into immutable code. For AI decisions, this ensures transparency and auditability of the entire governance lifecycle. The core mechanism is token-weighted voting, where a user's voting power is proportional to their holdings of a designated governance token (e.g., an ERC-20). This model aligns influence with economic stake, a common pattern in protocols like Compound and Uniswap. The contract must manage the state of proposals—including their description, voting period, and tally—and enforce rules like one vote per token and preventing double-voting.

We'll build a foundational contract using Solidity. Start by importing OpenZeppelin's ERC20 and Ownable contracts for the token standard and access control. The contract needs key data structures: a Proposal struct to store the proposal's id, description, voteCount, and status, and a mapping to track which addresses have voted on which proposals to prevent replay. An event, ProposalCreated, should be emitted for off-chain indexing. The constructor can initialize an admin and set a minimum voting period.

The core function is createProposal(string memory description), restricted to the contract owner or a designated proposer role. It should generate a new Proposal, assign it a unique ID, and emit an event. The vote(uint256 proposalId, uint256 amount) function is where token-weighting is implemented. It must check that the caller hasn't already voted, that the proposal is active, and that the caller's token balance is sufficient. It then records the vote and adds the amount to the proposal's voteCount. Use OpenZeppelin's SafeMath library or Solidity 0.8.x's built-in checks for arithmetic safety.

After the voting period ends, an executeProposal(uint256 proposalId) function can be called to finalize the outcome. It should verify the proposal is eligible for execution (e.g., voting ended and has sufficient quorum) and then update its status. For AI governance, the execution step could be as simple as emitting an event with the result, or it could call an external function on a separate AI model registry or oracle contract to enact the decision, such as upgrading a model version.

Critical security considerations include protecting against vote manipulation through snapshotting. A malicious token holder could transfer tokens after voting to gain additional voting power. Mitigate this by taking a snapshot of balances at the proposal creation block using a mechanism like OpenZeppelin's ERC20Snapshot, or by locking tokens during the voting period. Also, ensure the contract is upgradeable via a proxy pattern if governance rules need to evolve, and thoroughly audit all state-changing functions.

To test, deploy the contract to a testnet like Sepolia. Use a script to mint governance tokens, create a proposal, cast votes from different addresses, and execute the outcome. Tools like Hardhat or Foundry are ideal for writing comprehensive unit tests that simulate various attack vectors. The final contract provides a verifiable, on-chain record for AI governance, forming the backbone for decentralized oversight of model parameters, training data selection, or ethical guidelines.

Snapshot is a decentralized voting platform that enables gasless, off-chain governance. It uses a signed message strategy where voters sign messages with their wallets to cast votes, which are then recorded on IPFS and indexed by the Snapshot protocol. This is ideal for frequent, low-stakes governance decisions for an AI model, as it eliminates transaction fees and reduces voter friction. Your deployed ERC-20 token will serve as the voting power source, with one token equaling one vote.

To create your space, navigate to snapshot.org and connect the wallet that deployed your token (e.g., the owner). Click 'Create a Space'. You'll need to configure several key settings: the Space Name (e.g., your-ai-model.eth), your token's contract address, and the network (e.g., Sepolia). The most critical setting is the Voting Strategy. You will select the erc20-balance-of strategy and input your token's contract address. This strategy calculates voting power based on a user's token balance at a specific block number when a proposal is created.

You must define your Voting System and Proposal Validation. Snapshot supports several systems; for token-weighted voting, select single-choice or weighted voting. You will also set proposal thresholds in the space's settings, such as a minimum token requirement to submit a proposal. For example, you could require a user to hold at least 1000 $AIGOV tokens to create a proposal, preventing spam. These settings are managed in the 'Strategies' and 'Validation' tabs of your space's configuration.

Once your space is configured, you can create your first proposal. Click 'New Proposal' in your space. A proposal includes a title, description (using Markdown), the choices (e.g., For, Against, Abstain), and the snapshot block number. The snapshot block is crucial—it determines the token balances used for voting power, ensuring fairness by preventing last-minute token acquisitions. After publishing, voters can connect their wallets, see their voting power, and sign the message to cast their vote, with results tallied automatically.

To make your DAO's frontend interactive, integrate the Snapshot SDK or GraphQL API. You can fetch all proposals for your space to display them in your dApp. The key query involves the space name and the network. For on-chain execution of passed votes, you can use a relayer or safeSnap. SafeSnap bridges off-chain Snapshot results to on-chain execution via a Gnosis Safe, allowing trusted execution of proposals like upgrading a model's parameters or releasing funds from a treasury, completing the governance loop.

Quadratic voting is a mechanism where the cost of a vote increases quadratically with the number of votes cast on a single proposal. Instead of a simple 1-token-1-vote model, a user's voting power is calculated as the square root of their committed tokens. This design, popularized by projects like Gitcoin Grants, significantly reduces the influence of large token holders (whales) and better reflects the intensity of preference among a broader community. For an AI governance system, this ensures that decisions about model parameters, training data inclusion, or deployment rules aren't solely dictated by the wealthiest participants.

To implement this, you need to modify the voting power calculation in your smart contract. The core logic replaces a linear token balance check with a square root function. In Solidity, you can use a library like OpenZeppelin's Math library for secure, gas-efficient square root operations. The basic formula is: voting_power = sqrt(tokens_committed_to_vote). A user committing 100 tokens would have 10 voting power, while committing 10,000 tokens would yield only 100 voting power, making large-scale vote buying exponentially more expensive and less efficient.

Your contract must track the tokens a user commits to each specific proposal, as quadratic voting costs are per proposal. A typical pattern involves a vote function that transfers tokens into a temporary escrow for the proposal's duration. The critical check calculates the user's marginal cost for each additional vote: the cost of vote n is roughly 2n - 1 units of voting power. Always use a pull-over-push pattern for refunds to avoid reentrancy attacks and gas limit issues when the voting period ends and funds are returned.

Consider integrating a vote delegation feature that also respects quadratic scaling. When a delegate votes on behalf of others, the combined voting power should be the square root of the sum of all delegated tokens, not the sum of square roots. This prevents sybil attacks where users split tokens among multiple addresses to gain more influence. Auditing this math is crucial; consider using established libraries from governance systems like OpenZeppelin Governor or directly forking audited code from projects implementing QV.

Finally, front-end applications must clearly communicate this cost structure to users. The interface should show the estimated token cost for casting multiple votes on a single option, helping voters understand the non-linear trade-off. Transparently displaying the calculated voting power before transaction confirmation builds trust. This completes a foundational, sybil-resistant voting layer, setting the stage for connecting these on-chain signals to off-chain AI execution in the next step.

A well-defined proposal template is critical for efficient and secure DAO governance, especially for AI systems. It standardizes the information voters need, reduces ambiguity, and ensures all technical and ethical considerations are addressed before a vote. For AI decisions, templates must capture unique elements like model hashes, training data provenance, performance metrics, and resource requirements. Using a framework like OpenZeppelin's Governor, you can encode these templates directly into the proposal creation logic, ensuring every submission includes mandatory fields.

Start by defining the core structure. A robust AI proposal should include: Proposal Type (e.g., Model Upgrade, Parameter Tweak, Treasury Allocation), Technical Specification (IPFS hash of the model or config file), Performance Data (benchmark results against a test set), Resource Impact (estimated compute cost for inference/retraining), and Voting Parameters (quorum, voting delay, voting period). This structure moves discussions from subjective debates to objective evaluations of verifiable on-chain and off-chain data.

Implement this template in your governance contract. Below is a simplified example using a Solidity struct and a function to create proposals. This ensures every proposal stores the necessary AI-specific metadata on-chain.

solidityCopy
struct AIProposal {
    ProposalType pType;
    string modelHash; // IPFS CID of the model/weights
    string benchmarkData; // IPFS CID of evaluation results
    uint256 estimatedComputeCost;
    // ... other parameters
}

function createAIProposal(
    AIProposal calldata aiData,
    address[] memory targets,
    uint256[] memory values,
    bytes[] memory calldatas
) public returns (uint256 proposalId) {
    // Validate required AI data is provided
    require(bytes(aiData.modelHash).length > 0, "Missing model hash");
    // Create the core proposal via Governor
    proposalId = propose(targets, values, calldatas, aiData.description);
    // Store the AI-specific metadata linked to the proposalId
    _aiProposalData[proposalId] = aiData;
}

Integrate with decentralized storage for large files. The on-chain proposal should store only content identifiers (like IPFS CIDs) for the actual model binaries, datasets, and lengthy reports. This keeps transaction costs low while maintaining verifiable data integrity. Voters' interfaces can then fetch and display this off-chain data. Tools like IPFS, Arweave, or Filecoin are essential for this component. The proposal's smart contract logic should validate that these CIDs are provided and formatted correctly.

Finally, establish clear voting thresholds and periods tailored to the proposal's risk level. A major model upgrade might require a 60% supermajority and a 7-day voting period, while a minor parameter adjustment could pass with a simple majority and 3 days. These parameters should be adjustable via governance itself. By combining structured data templates, on-chain enforcement, and decentralized storage, you create a transparent and auditable decision-making process for complex AI systems.

A governance dashboard is the user interface that connects token holders to your on-chain voting smart contracts. Its core functions are to display active proposals, allow users to cast votes weighted by their token balance, and show real-time results. For AI governance, proposals typically involve decisions like model parameter updates, training data set approvals, or inference fee adjustments. Building this frontend requires interacting with your governance contract's functions, primarily createProposal, vote, and getProposalResults.

Start by setting up a frontend project with a Web3 library. Using Ethers.js v6 or Viem is standard. Connect to the user's wallet (e.g., MetaMask) to read their token balance and voting power. You'll need your governance contract's ABI and address. The dashboard should first fetch and list all proposals by calling a view function like getAllProposals(), displaying key data: proposal ID, description, voting deadline, and current tally of for and against votes.

The voting mechanism must calculate vote weight. In a simple system, weight equals the user's token balance at a specific block (snapshot). Your contract should store this snapshot. When a user votes, the frontend calls the vote(uint proposalId, bool support) function, signing the transaction with their wallet. For a better user experience, implement gas estimation and transaction status tracking. Consider using OpenZeppelin's Governor contract as a base, which standardizes these patterns and includes security features like vote delegation and timelocks.

To govern AI decisions, proposal creation must be clear. The dashboard should have a form for authorized proposers (e.g., those holding a minimum token threshold) to submit a new proposal. The description should link to off-chain specifications, like an IPFS hash containing the details of a new AI model version. The on-chain transaction will execute the createProposal function, which queues the vote and emits an event. Your frontend should listen for these events to update the proposal list in real-time using a provider library.

Finally, display results and execution. Once a voting period ends, the dashboard should call getProposalResults and show the outcome. If a proposal passes, there may be a timelock delay before the encoded function call (e.g., upgrading a model registry contract) can be executed. The dashboard can provide a button for any user to trigger the executeProposal function after the delay. For transparency, consider integrating The Graph to index proposal and vote data for efficient querying and historical analysis, creating a permanent record of all AI governance decisions.