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IAB AI-CONTROL Workshop Report
draft-iab-ai-control-report-00

Document Type Active Internet-Draft (iab)
Authors Mark Nottingham , Suresh Krishnan
Last updated 2024-11-04
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draft-iab-ai-control-report-00
Network Working Group                                      M. Nottingham
Internet-Draft                                                Cloudflare
Intended status: Informational                               S. Krishnan
Expires: 8 May 2025                                                Cisco
                                                         4 November 2024

                     IAB AI-CONTROL Workshop Report
                     draft-iab-ai-control-report-00

Abstract

   The AI-CONTROL Workshop was convened by the Internet Architecture
   Board (IAB) in September 2024.  This report summarizes its
   significant points of discussion and identifies topics that may
   warrant further consideration and work.

Status of This Memo

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   This Internet-Draft will expire on 8 May 2025.

Copyright Notice

   Copyright (c) 2024 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
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   Please review these documents carefully, as they describe your rights
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
     1.1.  Chatham House Rule  . . . . . . . . . . . . . . . . . . .   3
     1.2.  Views Expressed in this Report  . . . . . . . . . . . . .   3
   2.  Overview of the AI Crawling Landscape . . . . . . . . . . . .   3
     2.1.  Crawl Time vs. Inference Time . . . . . . . . . . . . . .   4
       2.1.1.  Multiple Uses for Crawl Data  . . . . . . . . . . . .   4
       2.1.2.  Application of Preferences  . . . . . . . . . . . . .   5
     2.2.  Trust . . . . . . . . . . . . . . . . . . . . . . . . . .   5
     2.3.  Attachment  . . . . . . . . . . . . . . . . . . . . . . .   6
       2.3.1.  robots.txt (and similar)  . . . . . . . . . . . . . .   6
       2.3.2.  Embedding . . . . . . . . . . . . . . . . . . . . . .   6
       2.3.3.  Registries  . . . . . . . . . . . . . . . . . . . . .   7
     2.4.  Vocabulary  . . . . . . . . . . . . . . . . . . . . . . .   8
   3.  Conclusions . . . . . . . . . . . . . . . . . . . . . . . . .   8
     3.1.  Potential Standards Work  . . . . . . . . . . . . . . . .   8
       3.1.1.  Out of Initial Scope  . . . . . . . . . . . . . . . .   9
   4.  Security Considerations . . . . . . . . . . . . . . . . . . .   9
   5.  Informative References  . . . . . . . . . . . . . . . . . . .   9
   Appendix A.  About the Workshop . . . . . . . . . . . . . . . . .   9
     A.1.  Agenda  . . . . . . . . . . . . . . . . . . . . . . . . .  10
       A.1.1.  Thursday 2024-09-19 . . . . . . . . . . . . . . . . .  10
       A.1.2.  Friday 2024-09-20 . . . . . . . . . . . . . . . . . .  10
     A.2.  Attendees . . . . . . . . . . . . . . . . . . . . . . . .  10
   IAB Members at the Time of Approval . . . . . . . . . . . . . . .  11
   Acknowledgements  . . . . . . . . . . . . . . . . . . . . . . . .  12
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  12

1.  Introduction

   The Internet Architecture Board (IAB) holds occasional workshops
   designed to consider long-term issues and strategies for the
   Internet, and to suggest future directions for the Internet
   architecture.  This long-term planning function of the IAB is
   complementary to the ongoing engineering efforts performed by working
   groups of the Internet Engineering Task Force (IETF).

   The Internet is one of the major sources of data used to train large
   language models (LLMs, or more generally "AI").  Because this use was
   not envisioned by most publishers of information on the Internet, a
   means of expressing the owners' preferences regarding AI crawling has
   emerged, sometimes backed by law (e.g., in the European Union's AI
   Act [AI-ACT]).

   The IAB convened the AI-CONTROL Workshop to "explore practical opt-
   out mechanisms for AI and build an understanding of use cases,
   requirements, and other considerations in this space."  [CFP] In

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   particular, the emerging practice of using the Robots Exclusion
   Protocol [RFC9309] -- also known as "robots.txt" -- has been
   uncoordinated, and may or may not be a suitable way to control AI
   crawlers.  However, discussion was not limited to consideration of
   robots.txt, and approaches other than opt-out were considered.

   To ensure many viewpoints were represented, the program committee
   invited a broad selection of technical experts, AI vendors, content
   publishers, civil society advocates, and policymakers.

1.1.  Chatham House Rule

   Participants agreed to conduct the workshop under the Chatham House
   Rule [CHATHAM-HOUSE], so this report does not attribute statements to
   individuals or organizations without express permission.  Most
   submissions to the workshop were public and thus attributable; they
   are used here to provide substance and context.

   Appendix A.2 lists the workshop participants, unless they requested
   that this information be witheld.

1.2.  Views Expressed in this Report

   This document is a report on the proceedings of the workshop.  The
   views and positions documented in this report are expressed during
   the workshop by participants and do not necessarily reflect IAB's
   views and positions.

   Furthermore, the content of the report comes from presentations given
   by workshop participants and notes taken during the discussions,
   without interpretation or validation.  Thus, the content of this
   report follows the flow and dialogue of the workshop but does not
   attempt to capture a consensus.

2.  Overview of the AI Crawling Landscape

   The workshop began by surveying the state of AI control.

   Currently, Internet publishers express their preferences for how
   their content is treated for purposes of AI training using a variety
   of mechanisms, including declarative ones, such as terms of service
   and robots.txt [RFC9309], and active ones, such as use of paywalls
   and selective blocking of crawlers (e.g., by IP address, User-Agent).

   There was disagreement about the implications of AI opt-out overall.
   Research indicates that the use of such controls is becoming more
   prevalent, reducing the availability of data for AI training.  She
   participants expressed concern about the implications of this --

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   although at least one AI vendor seemed less concerned by this,
   indicating that "there are plenty of tokens available" for training,
   even if many opt out.  Others expressed a need to opt out of AI
   training because of how they perceive its effects on their control
   over content, seeing AI as usurping their relationships with
   customers and a potential threat to whole industries.

   However, there was quick agreement that both viewpoints were harmed
   by the current state of AI opt-out -- a situation where "no one is
   better off" (in the words of one participant).

   Much of that dysfunction was attributed to the lack of coordination
   and standards for AI opt out.  Currently, content publishers need to
   consult with each AI vendor to understand how to opt out of training
   their products, as there is significant variance in each vendor's
   behaviour.  Furthermore, publishers need to continually monitor both
   for new vendors, and for changes to the policies of the vendors they
   are aware of.

   Underlying those immediate issues, however, are significant
   constraints that could be attributed to uncertainties in the legal
   context, the nature of AI, and the implications of needing to opt out
   of crawling for it.

2.1.  Crawl Time vs. Inference Time

   Perhaps most significant is the "crawl time vs. inference time"
   problem.  Statements of preference are apparent at crawl time, bound
   to content either by location (e.g. robots.txt) or embedded inside
   the content itself as metadata.  However, the target of those
   directives is often disassociated from the crawler, either because
   the crawl data is not only used for training AI models, or because
   the preferences are applicable at inference time.

2.1.1.  Multiple Uses for Crawl Data

   A crawl's data might have multiple uses because the vendor also has
   another product that uses it (e.g., a search engine), or because the
   crawl is performed by a party other than the AI vendor.  Both are
   very common patterns: operators of many Internet search engines also
   train AI models, and many AI models use third party crawl data.  In
   either case, conflating different uses can change the incentives for
   publishers to cooperate with the crawler.

   Well-established uses of crawling such as Internet search were seen
   by participants as at least partially aligned with the interests of
   publishers: they allow their sites to be crawled, and in return they
   receive higher traffic and attention due to being in the search

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   index.  However, several participants pointed out that this symbiotic
   relationship does not exist for AI training uses -- with some viewing
   AI as hostile to publishers, because it has the capacity to take
   traffic away from their sites.

   Therefore, when a crawler has multiple uses that include AI,
   participants observed that "collateral damage" was likely for non-AI
   uses, especially when publishers take more active control measures
   such as blocking or paywalls to protect their interests.

   Several participants expressed concerns about this phenomenon's
   effects on the ecosystem, effectively "locking down the Web" with one
   opining that there were implications on freedom of expression
   overall.

2.1.2.  Application of Preferences

   When data is used to train an LLM, the resulting model does not have
   the ability to only selectively use a portion of it when performing a
   task, because inference uses the whole model, and it is not possible
   to identify specific input data for its use in doing so.

   This means that while publishers preferences may be available when
   content is crawled, they generally are not when inference takes
   place.  Those preferences that are stated in reference to use by AI
   -- for example, "no military uses" or "non-commercial only" cannot be
   applied by a general-purpose "foundation" model.

   This leaves a few unappealing choices to AI vendors that wish to
   comply with those preferences.  They can simply omit such data from
   foundation models, thereby reducing their viability.  Or, they can
   create a separate model for each permutation of preferences -- with a
   likely proliferation of models as the set of permutations expands.

   Compounding this issue was the observation that preferences change
   over time, whereas LLMs are created over long time frames and cannot
   easily be updated to reflect those changes.  Of particular concern to
   some was how an opt-out regime makes the default stickier.

2.2.  Trust

   This disconnection between the statement of preferences and its
   application was felt by participants to contribute to a lack of trust
   in the ecosystem, along with the typical lack of attribution for data
   sources in LLMs, lack of an incentive for publishers to contribute
   data, and finally (and most noted) a lack of any means of monitoring
   compliance with preferences.

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   This lack of trust led some participations to question whether
   communicating preferences is sufficient in all cases without an
   accompanying way to mitigate or track cases of those preferences
   being followed.  Some participants also indicated that lack of trust
   was the primary cause of increasingly prevalent blocking of AI
   crawler IP addresses, among other measures.

2.3.  Attachment

   One of the primary focuses of the workshop was on _attachment_ -- how
   preferences are associated with content on the Internet.  A range of
   mechanisms was discussed.

2.3.1.  robots.txt (and similar)

   The Robots Exclusion Protocol [RFC9309] is widely recognised by AI
   vendors as an attachment mechanism for preferences.  Several
   deficiencies were discussed.

   First, it does not scale to offer granular control over large sites
   where authors might want to express different policies for a range of
   content (for example, YouTube).

   Robots.txt also is typically under the control of the site
   administrator.  If a site has content from many creators (as is often
   the case for social media and similar platforms), the administrator
   may not allow them to express their preferences fully, or at all.

   If content is copied or moved to a different site, the preferences at
   the new site need to be explicitly transferred, because robots.txt is
   a separate resource.

   These deficiencies led many participants to feel that robots.txt
   cannot be the only solution to opt-out: rather, it should be part of
   a larger system that addresses its shortcomings.

   Participants noted that other, similar attachment mechanisms have
   been proposed.  However, none appear to have gained as much attention
   or implementation (both by AI vendors and content owners) as
   robots.txt.

2.3.2.  Embedding

   Another mechanism for associating preferences with content is to
   embed them into the content itself.  Many formats used on the
   Internet allow this; for example, HTML has the <meta> tag, images
   have XMP and similar metadata sections, and XML and JSON have rich
   potential for extensions to carry such data.

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   Embedded preferences were seen to have the advantage of granularity,
   and of "travelling with" content as it is produced, when it is moved
   from site to site, or when it is stored offline.

   However, several participants pointed out that embedded preferences
   are easily stripped from most formats.  This is a common practice for
   reducing the size of a file (thereby improving performance when
   downloading it), and for assuring privacy (since metadata often leaks
   information unintentionally).

   Furthermore, some types of content are not suitable for embedding.
   For example, it is not possible to embed preferences into purely
   textual content, and Web pages with content from several producers
   (such as a social media or comments feed) cannot easily reflect
   preferences for each one.

   Participants noted that the means of embedding preferences in many
   formats would need to be determined by or coordinated with
   organisations outside the IETF.  For example, HTML and many image
   formats are maintained by external bodies.

2.3.3.  Registries

   In some existing copyright management regimes, it is already common
   to have a registry of works that is consulted upon use.  For example,
   this approach is often used for photographs, music, and video.

   Typically, registries use hashing mechanisms to create a
   "fingerprint" for the content that is robust to changes.

   Using a registry decouples the content in question from its location,
   so that it can be found even if moved.  It is also claimed to be
   robust against stripping of embedded metadata, which is a common
   practice to improve performance and/or privacy.

   However, several participants pointed out issues with deploying
   registries at Internet scale.  While they may be effective for
   (relatively) closed and well-known ecosystems such as commercial
   music publishing, applying them to a diverse and very large ecosystem
   like the Internet has proven problematic.

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2.4.  Vocabulary

   Another major focus area for the workshop was on _vocabulary_ -- the
   specific semantics of the opt-out signal.  Several participants noted
   that there are already many proposals for vocabularies, as well as
   many conflicting vocabularies already in use.  Several examples were
   discussed, including where existing terms were ambiguous, did not
   address common use cases, or were used in conflicting way by
   different actors.

   Although no conclusions regarding exact vocabulary were reached, it
   was generally agreed that a complex vocabulary is unlikely to
   succeed.

3.  Conclusions

   Participants seemed to agree that on its current path, the ecosystem
   is not sustainable.  As one remarked, "robots.txt is broken and we
   broke it."

   Legal uncertainty, along with fundamental limitations of opt-out
   regimes pointed out above, limit the effectiveness of any technical
   solution, which will be operating in a system unlike either
   robots.txt (where there is a symbiotic relationship between content
   owners and the crawlers) or copyright (where the default is
   effectively opt-in, not opt-out).

   However, the workshop ended with general agreement that positive
   steps could be taken to improve communication of preferences from
   content owners for AI use cases.  In discussion, it was evident that
   discovery of preferences from multiple attachment mechanisms is
   necessary to meet the diverse needs of content authors, and that
   therefore defining how they are combined is important.

   We outline a proposed standard program below.

3.1.  Potential Standards Work

   The following items were felt to be good starting points for IETF
   work:

   *  Attachment to Web sites by location (in robots.txt or a similar
      mechanism)
   *  Attachment via embedding in IETF-controlled formats (e.g., HTTP
      headers)
   *  Definition of a common core vocabulary
   *  Definition of the overall regime; e.g., how to combine preferences
      discovered from multiple attachment mechanisms

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   It would be expected that the IETF would coordinate with other SDOs
   to define embedding in other formats (e.g., HTML).

3.1.1.  Out of Initial Scope

   It was broadly agreed that it would not be useful to work on the
   following items, at least to begin with:

   *  Enforcement mechanisms for preferences
   *  Registry-based solutions
   *  Identifying or authenticating crawlers and/or content owners
   *  Audit or transparency mechanisms

4.  Security Considerations

   _TODO_

5.  Informative References

   [CHATHAM-HOUSE]
              Chatham House, "Chatham House Rule", n.d.,
              <https://www.chathamhouse.org/about-us/chatham-house-
              rule>.

   [CFP]      Internet Architecture Board, "IAB Workshop on AI-CONTROL",
              n.d.,
              <https://datatracker.ietf.org/group/aicontrolws/about/>.

   [PAPERS]   Internet Architecture Board, "IAB Workshop on AI-CONTROL
              Materials", n.d.,
              <https://datatracker.ietf.org/group/aicontrolws/
              materials/>.

   [AI-ACT]   European Parliament, "Regulation (eu) 2024/1689 of the
              European Parliament and of the Council", 13 June 2024,
              <https://eur-lex.europa.eu/eli/reg/2024/1689/oj>.

   [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/rfc/rfc9309>.

Appendix A.  About the Workshop

   The AI-CONTROL Workshop was held on 2024-09-19 and 2024-09-29 at
   Wilkinson Barker Knauer in Washington DC, USA.

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   Workshop attendees were asked to submit position papers.  These
   papers are published on the IAB website [PAPERS], unless the
   submitter requested it be withheld.

   The workshop was conducted under the Chatham House Rule
   [CHATHAM-HOUSE], meaning that statements cannot be attributed to
   individuals or organizations without explicit authorization.

A.1.  Agenda

   This section outlines the broad areas of discussion on each day.

A.1.1.  Thursday 2024-09-19

   Setting the stage  An overview of the current state of AI opt-out,
      its impact, and existing work in this space

   Lightning talks  A variety of perspectives from participants

A.1.2.  Friday 2024-09-20

   Opt-Out Attachment: robots.txt and beyond  Considerations in how
      preferences are attached to content on the Internet

   Vocabulary: what opt-out means  What information the opt-out signal
      needs to convey

   Discussion and wrap-up  Synthesis of the workshop's topics and how
      future work might unfold

A.2.  Attendees

   Attendees of the workshop are listed with their primary affiliation.
   Attendees from the program committee (PC) and the Internet
   Architecture Board (IAB) are also marked.

   *  Jari Arkko, Ericsson
   *  Hirochika Asai, Preferred Networks
   *  Farzaneh Badiei, Digital Medusa (PC)
   *  Fabrice Canel, Microsoft (PC)
   *  Lena Cohen, EFF
   *  Alissa Cooper, Knight-Georgetown Institute (PC, IAB)
   *  Marwan Fayed, Cloudflare
   *  Christopher Flammang, Elsevier
   *  Carl Gahnberg
   *  Max Gendler, The News Corporation
   *  Ted Hardie
   *  Dominique Hazaël-Massieux, W3C

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   *  Gary Ilyes, Google (PC)
   *  Sarah Jennings, UK Department for Science, Innovation and
      Technology
   *  Paul Keller, Open Future
   *  Elizabeth Kendall, Meta
   *  Suresh Krishnan, Cisco (PC, IAB)
   *  Mirja Kühlewind, Ericsson (PC, IAB)
   *  Greg Leppert, Berkman Klein Center
   *  Greg Lindahl, Common Crawl Foundation
   *  Mike Linksvayer, GitHub
   *  Fred von Lohmann, OpenAI
   *  Shayne Longpre, Data Provenance Initiative
   *  Don Marti, Raptive
   *  Sarah McKenna, Alliance for Responsible Data Collection; CEO,
      Sequentum
   *  Eric Null, Center for Democracy and Technology
   *  Chris Needham, BBC
   *  Mark Nottingham, Cloudflare (PC)
   *  Paul Ohm, Georgetown Law (PC)
   *  Braxton Perkins, NBC Universal
   *  Chris Petrillo, Wikimedia
   *  Sebastian Posth, Liccium
   *  Michael Prorock
   *  Matt Rogerson, Financial Times
   *  Peter Santhanam, IBM
   *  Jeffrey Sedlik, IPTC/PLUS
   *  Rony Shalit, Alliance For Responsible Data Collection; Bright Data
   *  Ian Sohl, OpenAI
   *  Martin Thomson, Mozilla
   *  Thom Vaughan, Common Crawl Foundation (PC)
   *  Kat Walsh, Creative Commons
   *  James Whymark, Meta

   The following participants requested that their identity and/or
   affiliation not be revealed:

   *  A government official

IAB Members at the Time of Approval

   Internet Architecture Board members at the time this document was
   approved for publication were:

   _TBC_

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Acknowledgements

   The Program Committee and the IAB would like to thank Wilkinson
   Barker Knauer for their generosity in hosting the workshop.

   We also thank our scribes for capturing notes that assisted in
   production of this report:

   *  Zander Arnao
   *  Andrea Dean
   *  Patrick Yurky

Authors' Addresses

   Mark Nottingham
   Cloudflare
   Prahran
   Australia
   Email: mnot@mnot.net
   URI:   https://www.mnot.net/

   Suresh Krishnan
   Cisco
   Email: suresh.krishnan@gmail.com

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