



AI Preferences                                                 B. Silver
Internet-Draft                                                   Advance
Intended status: Experimental                           8 September 2025
Expires: 12 March 2026


            Vocabulary For Expressing AI Substitutive Usage
               draft-silver-aipref-vocab-substitutive-00

Abstract

   This Internet Draft proposes a category entitled "AI Substitutive
   Use" which would enable parties to express a preference regarding how
   digital assets are used by automated processing systems, with a focus
   on post-training (inference-time) uses that are likely to result in
   the creation of AI-generated outputs that substitute for the original
   asset.  The proposal is for this category to nest within the larger
   category of Automated Processing, currently envisaged in the working
   group draft [AIPREF-VOCAB] (21 July 2025).

About This Document

   This note is to be removed before publishing as an RFC.

   The latest revision of this draft can be found at
   https://datatracker.ietf.org/doc/draft-silver-aipref-vocab-
   substitutive/.  Status information for this document may be found at
   https://datatracker.ietf.org/doc/draft-silver-aipref-vocab-
   substitutive/.

   Discussion of this document takes place on the AI Preferences Working
   Group mailing list (mailto:ai-control@ietf.org), which is archived at
   https://mailarchive.ietf.org/arch/browse/ai-control/.  Subscribe at
   https://www.ietf.org/mailman/listinfo/ai-control/.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
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   This Internet-Draft will expire on 12 March 2026.

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Table of Contents

   1.  Rationale . . . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Conventions and Definitions . . . . . . . . . . . . . . . . .   3
   3.  Vocabulary Definition . . . . . . . . . . . . . . . . . . . .   4
     3.1.  AI Substitutive Use Category (New)  . . . . . . . . . . .   4
   4.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   4
   5.  Normative References  . . . . . . . . . . . . . . . . . . . .   4
   6.  Informative References  . . . . . . . . . . . . . . . . . . .   5
   Author's Address  . . . . . . . . . . . . . . . . . . . . . . . .   5

1.  Rationale

   Existing mechanisms for expressing preferences, including those under
   consideration by the AI Preferences WG do not address concerns which
   have been strongly articulated about the practice of using digital
   assets as input to AI models to generate outputs which substitute for
   or undermine the value of the original assets.  This gap leaves a
   broad group of stakeholders (including creators, journalists and
   publishers) without a means to express a preference regarding a type
   of use which is already having a material adverse impact on their
   rights.  Developers and deployers of AI systems are also left without
   a clear, standardized preference signal regarding such uses, which
   results in "blunt" approaches to gathering such content - exposing
   them to legal risk.  This proposal intends to define a tailored
   preference category to address the specific need, improve visibility
   across the board and support continued broad access to information



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   and content.

   The use of digital assets for inferencing "in real time" is
   widespread as a means of improving the accuracy and contextual
   relevance of outputs, such as through the use of techniques such as
   Retrieval-Augmented Generation (RAG) [RAG2020].  The flipside of that
   value is that such outputs are inclined to substitute for or dilute
   the value of the original asset, which decreases user engagement with
   the original asset.  This harms revenue opportunities and undermines
   the ability of the owner or distributor of the original asset to
   connect directly with their intended audience.  For example, the use
   of journalistic material to create AI-generated summaries which have
   resulted in the substantial reduction of internet traffic to online
   publications.  In the longer term, this jeopardises the
   sustainability of those enterprises and the underlying incentives to
   create and publish such material.  To mitigate this, some are moving
   content behind paywalls and deploying other means of limiting open
   access - diminishing access to information and content.

   Should incentives to create diminish, AI innovation will also suffer
   as a result of less quality content on which to build a distribution
   funnel.  This would also undermine the sustainability and
   verifiability of news and information services relied upon by the
   public and government institutions.  Where the AI model or platform
   takes on the role of information gatekeeper and shaper, connections
   between the public and original sources can be severed (or warped),
   which undermines the ability and willingness of internet users to
   ensure what they are reading, hearing or watching matches the
   original source(s), allowing factual misrepresentations to propagate
   and go unchecked.

   Creators have also justifiably expressed the need for a preference
   that addresses the use of their assets to create derivative works "in
   the style of" such original assets.  Creators are harmed by the
   unfettered use of their works as inputs to AI Models to create
   outputs which dilute the market for their works, adopting distinctive
   elements and styles established by the creators themselves - which
   also harms their moral rights and interests to protect the integrity
   of their works and ensure attribution.

2.  Conventions and Definitions

   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 BCP
   14 [RFC2119] [RFC8174] when, and only when, they appear in all
   capitals, as shown here.




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   For the purposes of this document, the following terms are used:

   *  *Post-training (inference-time)*: Uses of an AI/ML model that
      occur after the model has been trained and frozen, typically when
      generating outputs in response to inputs at runtime.

   *  *Retrieval-Augmented Generation (RAG)*: A technique where external
      content is retrieved at query time and supplied to a model to
      condition the generated output.  This document references RAG as a
      common mechanism by which substitutive outputs may be produced
      [RAG2020].

3.  Vocabulary Definition

3.1.  AI Substitutive Use Category (New)

   The Act of using one or more assets as input to a trained AI/ML model
   (as opposed to the training of the model) which results in an output
   which incorporates, summarizes, aggregates or reproduces the assets,
   including stylistic elements thereof; provided, however that this
   category does not cover the use of a lawfully acquired digital asset
   where carried out directly by an end user (as opposed to a search
   application or bot) as input to a trained model to create a summary
   of such digital asset.

   The use of assets for AI Substitutive Use is a proper subset of
   Automated Processing usage [AIPREF-VOCAB].

   This category is distinct from AI Training or Generative AI Training,
   as it addresses uses that occur after a model has been trained,
   during inference.  It is also distinct from Search, which covers uses
   that direct users back to the original asset.  Substitutive Use, by
   contrast, describes outputs that replace, reduce the utility of, or
   make the source asset redundant to users by summarizing, reproducing,
   or restyling its contents.

   Consistent with that objective, this category would not apply where
   end users are summarising digital assets which they have already
   acquired, outside of the context of search or retrieving such assets
   from online locations in summarized form.

4.  IANA Considerations

   This document has no IANA actions.

5.  Normative References





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   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,
              <https://www.rfc-editor.org/rfc/rfc2119>.

   [RFC8174]  Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
              2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
              May 2017, <https://www.rfc-editor.org/rfc/rfc8174>.

   [RFC9309]  Koster, M., Illyes, G., Zeller, H., and L. Sassman,
              "Robots Exclusion Protocol", RFC 9309,
              DOI 10.17487/RFC9309, September 2022,
              <https://www.rfc-editor.org/info/rfc9309>.

6.  Informative References

   [AIPREF-VOCAB]
              IETF AI Preferences Working Group, "AI Preferences
              Vocabulary", Work in Progress, Internet-Draft, draft-ietf-
              aipref-vocab, 21 July 2025,
              <https://datatracker.ietf.org/doc/draft-ietf-aipref-
              vocab/>.

   [RAG2020]  Facebook AI Research, University College London, and New
              York University, "Retrieval-Augmented Generation for
              Knowledge-Intensive NLP Tasks", NeurIPS 2020, 2020,
              <https://proceedings.neurips.cc/paper/2020/
              hash/6b493230205f780e1bc26945df7481e5-Abstract.html>.

Author's Address

   Bradley Silver
   Advance
   United States of America
   Email: bsilver@advance.com
















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