How to Verify AI Results on Blockchain

As artificial intelligence (AI) becomes more prevalent in our society, it’s crucial to ensure that the results produced by AI algorithms are accurate and reliable.

Unfortunately, this is easier said than done, as it’s often difficult to verify the accuracy of AI models and algorithms.

This is where blockchain technology comes in. By using the transparency and immutability of the blockchain, it’s possible to create a verifiable system for AI results that is auditable and tamper-proof.

In this article, we’ll explore how smart contracts can be used to automate the verification of AI results on the blockchain, ensuring transparency and accuracy.

Storing AI Results on the Blockchain

The first step in creating a verifiable system for AI results is to store the results on the blockchain.

This can be done in various ways, depending on the use case.

For example, if we’re dealing with a machine learning model that produces a binary classification result (e.g., true/false), we can store the result on the blockchain as a hash value.

This hash value can then be used to verify the accuracy of the result without revealing the actual data used to produce it.

Here’s an example of how this could be done in Solidity, a programming language used for creating smart contracts on the Ethereum blockchain:

function storeHash(string memory hash) public {
    bytes32 hashBytes = keccak256(bytes(hash));
    storedHashes.push(hashBytes);
}

In this code snippet, we’re using the keccak256 hash function to convert the string value of the hash into a bytes32 value.

We then store this value in an array called storedHashes, which anyone can access on the blockchain.

Automating Verification with Smart Contracts

Now that we’ve stored the AI results on the blockchain, we need a way to verify the accuracy of the results. This is where smart contracts come in.

Smart contracts are self-executing programs that live on the blockchain. They can be programmed to automate a wide range of processes, including the verification of AI results.

Here’s an example of how a smart contract could be used to verify the accuracy of a machine-learning model:

function verifyResult(string memory data, string memory hash) public view returns(bool) {
    bytes32 dataHash = keccak256(bytes(data));
    for (uint i = 0; i < storedHashes.length; i++) {
        if (storedHashes[i] == hashBytes && dataHash == hashBytes) {
            return true;
        }
    }
    return false;
}

bytes32 hashBytes = keccak256(bytes(hash));

In this code snippet, we’re defining a function called verifyResult, which takes two arguments: the data used to produce the result and the hash value of the result.

We then use the keccak256 hash function to convert these values into bytes32 values.

Next, we iterate over the storedHashes array to find a match for the hash value. If we find a match, we compare the dataHash value to the hashBytes value.

If they match, we return true, indicating that the result is accurate. If we don’t find a match, we return false, indicating that the result is inaccurate.

Ensuring Data Privacy

One of the challenges of verifying AI results is ensuring data privacy. We want to be able to verify the accuracy of the result without revealing the actual data used to produce it.

We can use techniques such as homomorphic encryption and secure multi-party computation to address this challenge.

These techniques enable us to perform encrypted data computations without revealing the data itself.

Here’s an example of how secure multi-party computation could be used to verify AI results:

function verifyResult(string memory data, string memory hash, address[] memory participants) public view returns(bool) {
    bytes32 dataHash = keccak256(bytes(data));
    bytes32 hashBytes = keccak256(bytes(hash));
    for (uint i = 0; i < storedHashes.length; i++) {
        if (storedHashes[i] == hashBytes && dataHash == hashBytes) {
            // Use secure multi-party computation to compare the data to the actual result
            bool result = secureComparison.compare(data, actualResult, participants);
            return result;
        }
    }
    return false;
}

In this code snippet, we’re using a library called secureComparison to perform a secure comparison of the data used to produce the result and the actual result. This library uses secure multi-party computation techniques to compare without revealing the data.

Real World Scenario

A potential use case for verifying AI results on the blockchain is in the field of medical diagnostics. Medical diagnostics rely heavily on accurate and reliable AI algorithms, as misdiagnoses can seriously affect patients.

By storing the results of medical AI algorithms on the blockchain and using smart contracts to automate the verification process, creating a system that ensures the accuracy of diagnoses and transparency to patients and healthcare providers is possible.

Here’s an example of how this could work:

1. A patient goes to the doctor and undergoes a medical scan, such as an MRI.

2. The scan results are fed into an AI algorithm that produces a diagnosis.

3. The diagnosis is stored on the blockchain as a hash value using the Solidity code snippet mentioned earlier.

4. The patient and their healthcare provider can access the stored hash value on the blockchain to verify the accuracy of the diagnosis using the smart contract code snippet mentioned earlier.

5. If the diagnosis is accurate, the patient can proceed with treatment. If the diagnosis is inaccurate, the patient and their healthcare provider can investigate the cause of the inaccuracy and make any necessary adjustments to the AI algorithm.

By using the blockchain to store and verify the results of medical AI algorithms, patients and healthcare providers can have greater confidence in the accuracy of diagnoses.

Conclusion

As AI becomes more prevalent in our society, it’s crucial to ensure that the results produced by AI algorithms are accurate and reliable. By using blockchain technology and smart contracts, it’s possible to create a verifiable system for AI results that is auditable and tamper-proof.

In this article, we’ve explored how smart contracts can be used to automate the verification of AI results on the blockchain, ensuring transparency and accuracy.

We’ve also discussed how techniques such as homomorphic encryption and secure multi-party computation can be used to ensure data privacy while verifying AI results.

While there are still many challenges to be addressed in verifiable AI results, using blockchain technology and smart contracts is a promising step towards creating a more transparent and trustworthy system for AI.