Usage Preferences Vocabulary
2025-07-21
This section defines the controlled vocabulary used to express usage permissions or reservations under the TDM·AI Protocol. The vocabulary enables machine-readable communication of preferences regarding the use of digital content for automated processing activities, including AI training and generative AI training.
The TDM·AI vocabulary is based on the structure and semantics defined in the AI Preferences Vocabulary Internet-Draft (linked content may be subject to updates), developed by the IETF AI Preferences (aipref) Working Group, and aligned with the Attachment Mechanisms draft (linked content may be subject to updates).
The protocol adopts the same category structure and updated machine-readable keys defined in the IETF drafts:
Automated Processing (formerly tdm)
all
Top-level category covering all automated analysis and processing
AI Training
train-ai
General-purpose or task-specific training of AI models
Generative AI Training
train-genai
Training models to produce synthetic content
AI Use (Inference)
ai-use
Using assets as inputs to operate trained AI models
Search
search
Using assets in search engines or discovery applications
Each category may be declared independently. However, restrictions follow a strict hierarchy: opting out of a higher-level category (e.g., all) implies restriction of all its subordinate categories (train-ai, train-genai, ai-use, and search).
Vocabulary Definition
This section defines the categories of use in the vocabulary, quoted from the IETF , https://www.ietf.org/archive/id/draft-ietf-aipref-vocab-02.html#section-4.
1. Automated Processing Category
"The act of using one or more assets in the context of automated processing aimed at analysing text and data in order to generate information which includes but is not limited to patterns, trends and correlations.
The use of assets for automated processing encompasses all the subsequent categories."
2. AI Training Category
"The act of training machine learning models or artificial intelligence (AI).
The use of assets for AI Training is a proper subset of Automated Processing usage"
3. Generative AI Training Category
"The act of training general purpose AI models that have the capacity to generate text, images or other forms of synthetic content, or the act of training more specialised AI models that have the purpose of generating text, images or other forms of synthetic content.
The use of assets for Generative AI Training is a proper subset of AI Training usage."
4. AI Use Category
"The act of using one or more assets as input to a trained AI/ML model as part of the operation of that model (as opposed to the training of the model).
The use of assets for AI Use is a proper subset of Automated Processing usage."
5. Search Category
"Using one or more assets in a search application that directs users to the location from which the assets were retrieved."
The purpose of defining a distinct Search category is to allow preferences to be expressed about search applications, independent of other categories of use. A distinct Search category allows for preferences specific to search applications, even if the use of AI is involved in their implementation.
The use of assets for Search is a proper subset of Automated Processing usage."
Extension: Registry-Based Declarations
The IETF draft focuses on location-based expression of preferences (e.g., via robots.txt file or HTTP headers) it explicitly states.
The TDM·AI Protocol addresses this limitation by supporting registry-based declarations, enabling verifiable and persistent preference signaling even when content is redistributed or metadata is stripped. In this model:
Declarations are linked to content-derived identifiers (ISCC fingerprints).
Preferences are published to federated registries.
Anyone with access to the content can generate the ISCC fingerprint and verify the declared usage policy.
This architecture is fully compatible with the IETF vocabulary and enhances its practical applicability in federated, cross-platform environments.
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