Content Consent for AI/ML Data Mining
Video
Original
Abstract
This EIP proposes a standardized approach to declaring mining preferences for digital media content on the EVM-compatible blockchains. This extends digital media metadata standards like ERC-7053 and NFT metadata standards like ERC-721 and ERC-1155, allowing asset creators to specify how their assets are used in data mining, AI training, and machine learning workflows.
Motivation
As digital assets become increasingly utilized in AI and machine learning workflows, it is critical that the rights and preferences of asset creators and license owners are respected, and the AI/ML creators can check and collect data easily and safely. Similar to robot.txt to websites, content owners and creators are looking for more direct control over how their creativities are used.
This proposal standardizes a method of declaring these preferences. Adding dataMiningPreference
in the content metadata allows creators to include the information about whether the asset may be used as part of a data mining or AI/ML training workflow. This ensures the original intent of the content is maintained.
For AI-focused applications, this information serves as a guideline, facilitating the ethical and efficient use of content while respecting the creator's rights and building a sustainable data mining and AI/ML environment.
The introduction of the dataMiningPreference
property in digital asset metadata covers the considerations including:
- Accessibility: A clear and easily accessible method with human-readibility and machine-readibility for digital asset creators and license owners to express their preferences for how their assets are used in data mining and AI/ML training workflows. The AI/ML creators can check and collect data systematically.
- Adoption: As Coalition for Content Provenance and Authenticity (C2PA) already outlines guidelines for indicating whether an asset may be used in data mining or AI/ML training, it's crucial that onchain metadata aligns with these standards. This ensures compatibility between in-media metadata and onchain records.
Specification
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in RFC 2119 and RFC 8174.
This EIP introduces a new property, dataMiningPreference
, to the metadata standards which signify the choices made by the asset creators or license owners regarding the suitability of their asset for inclusion in data mining or AI/ML training workflows. dataMiningPreference
is an object that can include one or more specific conditions.
dataMining
: Allow the asset to be used in data mining for determining "patterns, trends, and correlations".aiInference
: Allow the asset to be used as input to a trained AI/ML model for inferring a result.aiGenerativeTraining
: Allow the asset to be used as training data for an AI/ML model that could produce derivative assets.aiGenerativeTrainingWithAuthorship
: Same asaiGenerativeTraining
, but requires that the authorship is disclosed.aiTraining
: Allow the asset to be used as training data for generative and non-generative AI/ML models.aiTrainingWithAuthorship
: Same asaiTraining
, but requires that the authorship is disclosed.
Each category is defined by a set of permissions that can take on one of three values: allowed
, notAllowed
, and constraint
.
allowed
indicates that the asset can be freely used for the specific purpose without any limitations or restrictions.notAllowed
means that the use of the asset for that particular purpose is strictly prohibited.constrained
suggests that the use of the asset is permitted, but with certain conditions or restrictions that must be adhered to.
For instance, the aiInference
property indicates whether the asset can be used as input for an AI/ML model to derive results. If set to allowed
, the asset can be utilized without restrictions. If notAllowed
, the asset is prohibited from AI inference.
If marked as constrained
, certain conditions, detailed in the license document, must be met. When constraint
is selected, parties intending to use the media files should respect the rules specified in the license. To avoid discrepancies with the content license, the specifics of these constraints are not detailed within the schema, but the license reference should be included in the content metadata.
Schema
The JSON schema of dataMiningPreference
is defined as follows:
{ "type": "object", "properties": { "dataMining": { "type": "string", "enum": ["allowed", "notAllowed", "constrained"] }, "aiInference": { "type": "string", "enum": ["allowed", "notAllowed", "constrained"] }, "aiTraining": { "type": "string", "enum": ["allowed", "notAllowed", "constrained"] }, "aiGenerativeTraining": { "type": "string", "enum": ["allowed", "notAllowed", "constrained"] }, "aiTrainingWithAuthorship": { "type": "string", "enum": ["allowed", "notAllowed", "constrained"] }, "aiGenerativeTrainingWithAuthorship": { "type": "string", "enum": ["allowed", "notAllowed", "constrained"] } }, "additionalProperties": true }
Examples
The mining preference example for not allowing generative AI training:
{ "dataMiningPreference": { "dataMining": "allowed", "aiInference": "allowed", "aiTrainingWithAuthorship": "allowed", "aiGenerativeTraining": "notAllowed" } }
The mining preference example for only allowing for AI inference:
{ "dataMiningPreference": { "aiInference": "allowed", "aiTraining": "notAllowed", "aiGenerativeTraining": "notAllowed" } }
The mining preference example for allowing generative AI training if mentioning authorship and follow license:
{ "dataMiningPreference": { "dataMining": "allowed", "aiInference": "allowed", "aiTrainingWithAuthorship": "allowed", "aiGenerativeTrainingWithAuthorship": "constrained" } }
Example Usage with ERC-721
The following is an example of using the dataMiningPreference
property in ERC-721 NFTs.
We can put the dataMiningPreference
field in the NFT metadata below. The license
field is only an example for specifying how to use a constrained condition, and is not defined in this proposal. A NFT has its way to describe its license.
{ "name": "The Starry Night, revision", "description": "Recreation of the oil-on-canvas painting by the Dutch Post-Impressionist painter Vincent van Gogh.", "image": "ipfs://bafyaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa", "dataMiningPreference": { "dataMining": "allowed", "aiInference": "allowed", "aiTrainingWithAuthorship": "allowed", "aiGenerativeTrainingWithAuthorship": "constrained" }, "license": { "name": "CC-BY-4.0", "document": "https://creativecommons.org/licenses/by/4.0/legalcode" } }
Example Usage with ERC-7053
The example using the dataMiningPreference
property in onchain media provenance registration defined in ERC-7053.
Assuming the Decentralized Content Identifier (CID) is bafyaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa
. We can put the dataMiningPreference
field in the Commit data directly. After following up the CID, got the Commit data:
{ "dataMiningPreference": { "dataMining": "allowed", "aiInference": "allowed", "aiTrainingWithAuthorship": "allowed", "aiGenerativeTrainingWithAuthorship": "constrained" }, "license": { "name": "CC-BY-4.0", "document": "https://creativecommons.org/licenses/by/4.0/legalcode" } }
We can also put the dataMiningPreference
field in any custom metadata whose CID is linked in the Commit data. The assetTreeCid
field is an example for specifying how to link a custom metadata. After following up the CID, got the Commit data:
{ /* custom metadata CID */ "assetTreeCid": "bafybbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb" }
Following up the assetTreeCid
which describes the custom properties of the registered asset:
{ "dataMiningPreference": { "dataMining": "allowed", "aiInference": "allowed", "aiTrainingWithAuthorship": "allowed", "aiGenerativeTrainingWithAuthorship": "constrained" }, "license": { "name": "CC-BY-4.0", "document": "https://creativecommons.org/licenses/by/4.0/legalcode" } }
Rationale
The technical decisions behind this EIP have been carefully considered to address specific challenges and requirements in the digital asset landscape. Here are the clarifications for the rationale behind:
- Adoption of JSON schema: The use of JSON facilitates ease of integration and interaction, both manually and programmatically, with the metadata.
- Detailed control with training types: The different categories like
aiGenerativeTraining
,aiTraining
, andaiInference
let creators control in detail, considering both ethics and computer resource needs. - Authorship options included: Options like
aiGenerativeTrainingWithAuthorship
andaiTrainingWithAuthorship
make sure creators get credit, addressing ethical and legal issues. - Introduction of
constrained
category: The introduction ofconstrained
category serves as an intermediary betweenallowed
andnotAllowed
. It signals that additional permissions or clarifications may be required, defaulting tonotAllowed
in the absence of such information. - C2PA alignment for interoperability: The standard aligns with C2PA guidelines, ensuring seamless mapping between onchain metadata and existing offchain standards.
Security Considerations
When adopting this EIP, it’s essential to address several security aspects to ensure the safety and integrity of adoption:
- Data Integrity: Since this EIP facilitates the declaration of mining preferences for digital media assets, the integrity of the data should be assured. Any tampering with the
dataMiningPreference
property can lead to unauthorized data mining usage. Blockchain's immutability will play a significant role here, but additional security layers, such as cryptographic signatures, can further ensure data integrity. - Verifiable Authenticity: Ensure that the individual or entity setting the
dataMiningPreference
is the legitimate owner or authorized representative of the digital asset. Unauthorized changes to preferences can lead to data misuse. Cross-checking asset provenance and ownership becomes paramount. Services or smart contracts should be implemented to verify the authenticity of assets before trusting thedataMiningPreference
. - Data Privacy: Ensure that the process of recording preferences doesn't inadvertently expose sensitive information about the asset creators or owners. Although the Ethereum blockchain is public, careful consideration is required to ensure no unintended data leakage.
Copyright
Copyright and related rights waived via CC0.
Adopted by projects
Not miss a beat of EIPs' update?
Subscribe EIPs Fun to receive the latest updates of EIPs Good for Buidlers to follow up.
View all