On-Chain Attribution for AI-Generated Educational Content: Leveraging ERC-8021 Builder Codes

# On-Chain Attribution for AI-Generated Educational Content: Leveraging ERC-8021 Builder Codes

As Large Language Models (LLMs) become increasingly proficient at synthesizing information and generating content, the question of attribution becomes paramount.  When an LLM creates educational material based on academic research, how do we ensure the original authors receive due credit? This article details a solution implemented on the Base chain: embedding ERC-8021 builder codes within transaction calldata to create a verifiable link between generated content and its source material. This approach establishes a system where the creators of the original research benefit directly from the downstream use of their work by AI.

## The Problem: Attribution in the Age of AI

The rapid advancement of LLMs like GPT-4 and Gemini presents a unique challenge to traditional attribution models.  These models don't simply *copy* content; they *synthesize* it, creating new text based on patterns learned from vast datasets. While citing sources in the generated content is a common practice, it’s often insufficient. Citations can be easily overlooked or removed, and the connection between the generated text and the original source can be tenuous.  Moreover, traditional academic metrics (like citation counts) may not adequately reflect the impact of research used to train these models.  This is particularly relevant as LLMs are used to create educational resources, where clear attribution is essential for maintaining academic integrity and recognizing intellectual contributions.

Researchers have explored various methods for addressing this challenge.  For instance, work in digital watermarking (e.g.,  [“Watermark the AI: Identifying Generated Text” by Kirchenbauer et al.](https://arxiv.org/abs/2301.10214)) aims to embed subtle signals within generated text to identify its origin. However, these watermarks can be fragile and susceptible to removal.  Similarly, provenance tracking systems, like those based on blockchain, have been proposed (e.g., [“Provenance: Decentralized Data Lineage for Machine Learning” by Basily et al.](https://arxiv.org/abs/1912.06388)), but often require significant infrastructure changes and may not be easily integrated into existing LLM workflows.

## The Solution: ERC-8021 Builder Codes

The approach implemented on Base leverages the flexibility of ERC-8021, a standard for builder inclusion in transactions. ERC-8021 allows users to specify conditions that must be met for their transaction to be included in a block. This functionality can be repurposed to embed metadata within the transaction calldata itself, creating a permanent, on-chain record of attribution.

Specifically, a schema 0 format is used, appending attribution codes as a suffix to the transaction calldata. These codes encode crucial information:

*   **Cogito Agent Code:** Identifies the specific AI agent responsible for generating the content.
*   **arXiv Paper IDs:** Lists the identifiers of the academic papers used as source material.
*   **GitHub Repo Identifiers:**  Includes links to relevant code repositories.
*   **First Author Names:** Records the names of the first authors of the cited papers.

This creates a verifiable chain from the generated content back to the original knowledge creators. Because the transaction is recorded on the blockchain, the attribution is immutable and publicly auditable.  

## Practical Implementation

The implementation involves modifying the transaction creation process to append the ERC-8021 builder code to the calldata. While a direct, public code repository isn’t provided, the concept is implemented within the Cogito framework.  The key idea is to serialize the attribution data into a compact format and append it to the transaction.  Consider a simplified Python example demonstrating how this data could be encoded:

```python
import hashlib

def encode_attribution(agent_code, paper_ids, repo_ids, author_names):
    data = f"{agent_code}:{','.join(paper_ids)}:{','.join(repo_ids)}:{','.join(author_names)}"
    encoded_data = hashlib.sha256(data.encode()).hexdigest()[:8] # Use a hash for brevity
    return encoded_data

# Example Usage
agent_code = "cogito-edu-v1"
paper_ids = ["2301.10214", "1912.06388"]
repo_ids = ["https://github.com/example/watermark-ai", "https://github.com/example/provenance-ml"]
author_names = ["Kirchenbauer", "Basily"]

attribution_code = encode_attribution(agent_code, paper_ids, repo_ids, author_names)
print(f"Attribution Code: {attribution_code}")

# This code would then be appended to the transaction calldata.
```

This example demonstrates the principle of encoding the relevant information into a concise string and hashing it for brevity. In a real-world scenario, the encoding would need to be more robust and handle various data types and potential edge cases. The resulting `attribution_code` would be appended to the calldata of a transaction submitted to the Base chain.

## Trade-offs and Alternatives

While this approach offers several benefits, it's important to consider its limitations and potential alternatives:

*   **Calldata Costs:** Appending data to calldata increases transaction costs.  This is a significant consideration, especially for high-volume applications.  Careful optimization of the encoding scheme is crucial to minimize the overhead.
*   **Calldata Size Limits:** Ethereum-compatible blockchains have limits on the maximum calldata size.  The amount of attribution data must be carefully managed to stay within these limits.
*   **Complexity:** Integrating ERC-8021 builder codes into existing workflows adds complexity to the transaction creation process.

Alternatives include:

*   **Off-Chain Provenance Tracking:** Maintaining a separate database or system to track provenance information. This approach is less secure and transparent than on-chain solutions.
*   **Digital Watermarking:** Embedding watermarks directly into the generated content.  As mentioned earlier, these watermarks can be fragile.
*   **Layer-2 Solutions:** Utilizing Layer-2 scaling solutions to reduce transaction costs and increase calldata capacity.

## Conclusion

Embedding ERC-8021 builder codes into transactions represents a promising approach to addressing the challenge of attribution in the age of AI-generated content. By creating a verifiable, on-chain record of provenance, this solution can help ensure that original researchers receive due credit for their work. While trade-offs exist, the benefits of transparency, immutability, and public auditability make this a compelling option for educational applications and beyond. As LLMs continue to evolve, innovative solutions like this will be essential for fostering a responsible and equitable AI ecosystem.