



AIPref                                                          F. Badii
Internet-Draft                                            Digital Medusa
Intended status: Informational                                 L. Bailey
Expires: 30 May 2026                                    Internet Archive
                                                                 J. Levy
                                                        26 November 2025


               AI Preferences Signaling: End User Impact
                    draft-farzdusa-aipref-enduser-00

Abstract

   Standards can have a major impact on end users across technological,
   legal, ethical, and governance dimensions, largely centering around
   access to information, control over their digital contributions, and
   data privacy.  The purpose of this Internet Draft is to document the
   potential impact of signaling AI preferences on end users other than
   publishers, and to suggest some principles for the ai-pref working
   group to consider when assessing proposed vocabulary and definitions
   IETF wishes to standardize for signaling.

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://farzaneh-
   unicode.github.io/enduser-AIpref/draft-farzdusa-aipref-enduser.html
   Status information for this document may be found at
   https://datatracker.ietf.org/doc/draft-farzdusa-aipref-enduser/.

   Source for this draft and an issue tracker can be found at
   https://github.com/Farzaneh-Unicode/enduser-AIpref.

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
   Task Force (IETF).  Note that other groups may also distribute
   working documents as Internet-Drafts.  The list of current Internet-
   Drafts is at https://datatracker.ietf.org/drafts/current/.

   Internet-Drafts are draft documents valid for a maximum of six months
   and may be updated, replaced, or obsoleted by other documents at any
   time.  It is inappropriate to use Internet-Drafts as reference
   material or to cite them other than as "work in progress."



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   This Internet-Draft will expire on 30 May 2026.

Copyright Notice

   Copyright (c) 2025 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
   Provisions Relating to IETF Documents (https://trustee.ietf.org/
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   Please review these documents carefully, as they describe your rights
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   provided without warranty as described in the Revised BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Who are the end users?  . . . . . . . . . . . . . . . . . . .   3
   3.  The IETF’s mandate and the Approach to Signaling AI
           Preferences . . . . . . . . . . . . . . . . . . . . . . .   4
   4.  The Right to Access Knowledge and Avoiding Internet
           Fragmentation . . . . . . . . . . . . . . . . . . . . . .   5
     4.1.  Access to Information . . . . . . . . . . . . . . . . . .   5
     4.2.  Intellectual Property Overreach and Rights Delay  . . . .   5
     4.3.  Risk of Web Fragmentation . . . . . . . . . . . . . . . .   5
     4.4.  Impact on AI Tool Utility . . . . . . . . . . . . . . . .   6
     4.5.  Site operators don’t own everything on their website  . .   6
   5.  Impact on User Rights, Transformation, and Academic
           Research  . . . . . . . . . . . . . . . . . . . . . . . .   6
     5.1.  Robots Rights to Read . . . . . . . . . . . . . . . . . .   6
     5.2.  Use in the public interest  . . . . . . . . . . . . . . .   6
   6.  Shortcomings of Restricting RAG and Inference . . . . . . . .   7
     6.1.  Collateral Damage to End Users and Beneficial
           Applications  . . . . . . . . . . . . . . . . . . . . . .   7
     6.2.  Risk to Automated Tools and Access to Knowledge (A2K) . .   7
     6.3.  Discouraging Personal Use of Automation . . . . . . . . .   7
     6.4.  Accessibility Concerns  . . . . . . . . . . . . . . . . .   8
     6.5.  Impact on Research and Flexibility  . . . . . . . . . . .   8
   7.  Technical and Definitional Instability  . . . . . . . . . . .   8
     7.1.  The Crawl Time vs. Inference  . . . . . . . . . . . . . .   8
     7.2.  Difficulty in Allowing Non-Commercial and Excluding
           Commercial Crawling . . . . . . . . . . . . . . . . . . .   8
     7.3.  Uncertainty in Definition . . . . . . . . . . . . . . . .   9
     7.4.  Asset level signaling . . . . . . . . . . . . . . . . . .   9
   8.  Case Studies  . . . . . . . . . . . . . . . . . . . . . . . .   9
     8.1.  Inhibition of Not-for-Profit Crawlers . . . . . . . . . .   9



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     8.2.  Opaque Robot Defenses . . . . . . . . . . . . . . . . . .   9
     8.3.  Collateral Damage to Beneficial Uses  . . . . . . . . . .   9
     8.4.  Threat to Public Archiving  . . . . . . . . . . . . . . .  10
     8.5.  Intermediaries Choosing for Smaller Site Operators  . . .  10
     8.6.  Platform Control over Creator Intent  . . . . . . . . . .  10
     8.7.  Creation of Quasi-IP Rights . . . . . . . . . . . . . . .  10
     8.8.  The Threat of Exclusionary Defaults . . . . . . . . . . .  10
   9.  The Impact on the Internet  . . . . . . . . . . . . . . . . .  11
   10. Proposed End User Impact Principles . . . . . . . . . . . . .  11
   11. Conclusion  . . . . . . . . . . . . . . . . . . . . . . . . .  12
   12. Conventions and Definitions . . . . . . . . . . . . . . . . .  12
   13. Security Considerations . . . . . . . . . . . . . . . . . . .  12
   14. IANA Considerations . . . . . . . . . . . . . . . . . . . . .  12
   15. Normative References  . . . . . . . . . . . . . . . . . . . .  12
   Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . .  13
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  13

1.  Introduction

   As AI systems start becoming a primary way people access and interact
   with information online, mechanisms for signaling AI preferences risk
   shaping far more than technical coordination.  While the AI-Pref
   Working Group focuses on enabling content hosts to express their
   preferences, these signals operate at the protocol layer, where they
   can quietly restrict access, chill lawful uses, and unintentionally
   fragment the open web.  End users, understood broadly in RFC 8890 as
   the human beings whose activities Internet standards support, include
   not only publishers and AI developers but also researchers,
   educators, people with disabilities, small creators, and the global
   public.  Many of these communities rely on rights to access,
   transform, and analyze information that could be undermined if
   preference signaling is interpreted or implemented as a form of
   control.  This draft outlines the potential impact of AI signaling on
   end users and proposes principles to ensure that standardization
   preserves interoperability, openness, and user agency.

2.  Who are the end users?

   Mark Nottingham the author of The Internet is for the End Users,
   proposes a definition for end users in RFC 8890: "end users" means
   human users whose activities IETF standards support, sometimes
   indirectly….End users are not necessarily a homogenous group; they
   might have different views of how the Internet should work and might
   occupy several roles, such as a seller, buyer, publisher, reader,
   service provider, and consumer.  An end user might browse the Web,
   monitor remote equipment, play a game, videoconference with
   colleagues, send messages to friends, or perform an operation in a
   remote surgery theater.  They might be "at the keyboard" or



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   represented by software indirectly (e.g., as a daemon)".

   This broad definition is especially useful to keep in mind as the
   outputs from the AI preferences working group are likely to have an
   impact on a broad range of end users.  The dominant voices in the AI-
   pref working group so far have been “publishers” and AI developers,
   each of which are a kind of end user.  Other end users’ rights,
   interests, and access have been discussed less frequently.  When
   rights and interests are discussed, in general the rights under
   consideration are those of Intellectual Property Rightsholders, while
   the discussions rarely mention other rightsholders, such as people
   with rights to access to knowledge and essential services, or
   interested parties.

   There is a need to ensure that property and economic interests are
   aligned with fundamental human rights, such as the right to freedom
   of opinion and expression under Article 19 of the Universal
   Declaration of Human Rights.  Intellectual-property and market-based
   mechanisms should function in a way that supports, rather than
   constrains, the free circulation of information and ideas.

   While AI Preference signaling mechanisms are intended to give content
   hosts (or site operators) a means to signal how content is used by AI
   systems, they can result in unintended consequences that
   disproportionately harm end users, including researchers, people with
   disabilities, and commercial and non-commercial actors, thereby
   undermining the open Internet.  Site operators may signal
   restrictions over content or uses that they do not have the legal
   right to control, such as text and data mining permitted under
   copyright exceptions, accessibility tools, public-domain materials,
   or facts and information not protected by intellectual property
   rights.  When interpreted strictly by AI systems, such signals can
   create a chilling effect: researchers, accessibility developers, and
   others may refrain from lawful uses simply because they believe the
   content is off-limits.  This misalignment between technical signals
   and actual rights risks fragmenting access to knowledge and
   undermining the open Internet.

3.  The IETF’s mandate and the Approach to Signaling AI Preferences

   The IETF was never intended to serve as a forum for protecting
   business models from technological innovation or enforcing copyright
   regimes.  Its purpose is to maintain and advance the interoperability
   of the Internet, to ensure that diverse systems, services, and users
   can communicate seamlessly across technical and institutional
   boundaries.  A shift away from this focus risks turning what should
   be a technical coordination effort into a venue for regulating reuse
   at the protocol layer, well before any legal reasoning or due-process



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   safeguards can apply.  If the IETF shifts toward a regime that
   encourages blanket prohibitions on data processing, regardless of
   context, consequence, or user benefit, we risk hardcoding a “chilling
   effect” into the infrastructure of the web itself.  And unlike lawful
   reuses, which are typically adjudicated through downstream legal
   reasoning, signaling operates at the technical layer, where denial is
   fast, quiet, and hard to contest.

4.  The Right to Access Knowledge and Avoiding Internet Fragmentation

   A major concern for end users is the potential restriction of access
   to information and essential services due to overly restrictive
   content signaling instituted by hosting platforms, creators and
   publishers.

4.1.  Access to Information

   People globally are increasingly accessing essential services and
   knowledge through the Internet.  AI-enabled applications could
   potentially simplify access to knowledge and essential services
   provided online, such as healthcare and other services.  However, if
   signaling AI preferences reinforced by regulatory reference become
   too restrictive, it can have a chilling effect and the fundamental
   right to access information and essential services could be harmed.

4.2.  Intellectual Property Overreach and Rights Delay

   Theorists of the Access to Knowledge (A2K) movement contend that
   current levels of Intellectual Property (IP) protection may be
   unbalanced regarding the right to access.  They argue that material
   interests are not simply equivalent to current IP provisions, and
   that "rights delayed are rights denied".  (Lea Shaver The Right to
   Science and Culture, 2010 Wis. L.  Rev. 121 (2010).

4.3.  Risk of Web Fragmentation

   There is a tension between giving site operators and declarants
   (those who declare their preferences about AI data usage) granular
   control over content and maintaining an open Internet.  Implementing
   very restrictive mechanisms risks "building fortress walls around
   content" and sacrificing the open web, potentially throttling the
   future of knowledge and access to essential services.









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4.4.  Impact on AI Tool Utility

   While large AI companies are the primary targets of restrictive
   preference signals, these controls can inadvertently restrict and
   harm end users who wish to use AI tools for common tasks like
   exploring content, summarizing, or translating documents or making
   services more accessible.  Setting a high bar for accessing and using
   information could negatively affect individual end users' ability to
   utilize these tools and overall experience when accessing the world
   wide web.  Ultimately, it is individual people who will pay the price
   for restricted access to AI tools, both financially and by IETF-
   endorsed limits on their ability to use AI to access the knowledge
   and information they seek.  Open source AI developers and others can
   also be disenfranchised if the preferences signals are not coherently
   implementable or if they have chilling effects.

4.5.  Site operators don’t own everything on their website

   Any given web page has the technical capacity to contain content from
   a variety of different rightsholders.  A large platform can therefore
   contain content from millions, even billions, of different content
   creators.  Giving a website owner the ability to determine the AI
   preferences for potentially millions of different creators and their
   works risks centralizing a huge amount of control.

5.  Impact on User Rights, Transformation, and Academic Research

   End users who are researchers are frequently hindered by licensing
   and contractual barriers, even when the underlying uses might be
   legally permissible under copyright law.

5.1.  Robots Rights to Read

   Researchers and other end users face a tension when downloading and
   human (consumptive) reading is allowed, but machine (non-consumptive)
   reading is disallowed, leading to the slogan “let the robots read.”
   Robots are perhaps always the agents of human users and preventing
   them from reading could ultimately prevent the humans from access.

5.2.  Use in the public interest

   The principle of allowing public interest uses of information has
   long supported knowledge production and freedom of expression.  These
   uses are without payment nor permission, rejecting the idea that
   access to information is "a privilege reserved for the well-behaved".
   In the AI-Pref discussions, many participants expressed a desire to
   differentiate between traditional search engine indexing—generally
   accepted as beneficial to the open web—and newer AI-enabled



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   summarization or answer generation.  Some content providers signaled
   that they may be open to appearing in classic search results but not
   in AI-driven outputs that repackage or synthesize their material.
   However, this signaling raises a deeper structural issue: by opting
   out, publishers and intellectual property rights holders and hosts
   effectively shift the burden of determining whether a use is
   permitted to the recipient of the signal.  While this may not be a
   significant obstacle for well-resourced actors like major platforms,
   it imposes disproportionate risks on small operators, open-source AI
   developers, and actors from the global majority, those who often lack
   the legal infrastructure, funding, or access to navigate complex
   copyright frameworks.

6.  Shortcomings of Restricting RAG and Inference

   Restricting RAG and inference, uses that occur after the foundation
   model has been trained, is fundamentally challenging due to the fluid
   nature of these processes and the risk of unintended negative
   consequences for legitimate users and access to information.

6.1.  Collateral Damage to End Users and Beneficial Applications

   One of the most significant shortcoming of restricting inference is
   the unintended curtailment of beneficial applications used everyday.

6.2.  Risk to Automated Tools and Access to Knowledge (A2K)

   If restrictions on inference are applied too broadly, end users who
   wish to use AI tools for common tasks like discovering content,
   summarizing documents, or translation would be barred from doing so.
   This directly undermines the end user's right to access knowledge
   (A2K).

6.3.  Discouraging Personal Use of Automation

   End users want to benefit from the efficiencies of automation, such
   as using bulk operations and automated search features, rather than
   having to rely solely on large, proprietary AI company tools.
   Placing the same restrictive bar on individual end users as on large
   AI developers for using information could negatively affect the
   ability of individuals to leverage these tools for personal or
   beneficial research purposes.









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6.4.  Accessibility Concerns

   Overly restrictive inference controls, such as a complete opt-out of
   “all data mining, automation and processing”, carry the risk of
   affecting uses like spam filtering, harmful language detection, and
   accessibility tools.  For instance, if a tool that describes a
   website to a visually impaired user relies on inference, a broad
   restriction could prevent that legitimate use.

6.5.  Impact on Research and Flexibility

   Researchers, when analyzing data, may be hindered if limitations
   block non-consumptive reading (TDM) but allow human reading.  The
   core issue remains that an overly restrictive environment risks
   constraining scholarship and academic communication.

7.  Technical and Definitional Instability

   The attempt to restrict RAG/Inference runs into technical limitations
   regarding scope, authority, and implementation methods.

7.1.  The Crawl Time vs. Inference

   Preference signals (like those in robots.txt or metadata) are
   available at crawl time, but they are often disassociated from the
   model when the content is actually used at inference time.  This
   could create mixed signals as to what the end user can do regarding
   the crawling vs inference and signaling about inference because of
   this time lag can create many problems for the end users, real time
   translation for example might not happen.

7.2.  Difficulty in Allowing Non-Commercial and Excluding Commercial
      Crawling

   For a general-purpose foundational model (LLM), it is technically
   difficult to distinguish between commercial and non-commercial uses
   at either crawl or inference time.  Because models cannot selectively
   isolate or invoke portions of their training data based on the
   eventual use context, a blanket “No AI” or similar restrictive signal
   tends to apply universally — even to lawful, commercial and non-
   commercial, or public-interest uses.  This leaves developers and
   researchers with “unappealing choices,” such as omitting the data
   entirely or creating separate models for every potential use
   category, both of which are impractical and risk excluding socially
   beneficial applications.






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7.3.  Uncertainty in Definition

   RAG is only one technique, and definitions of inference-time use are
   unstable.  The concept of "inference" is broad and covers a massive,
   conceptually fuzzy range of uses of material after training, making
   it difficult to develop a robust vocabulary term.  If standards focus
   too much on a specific technique like RAG, they risk becoming
   irrelevant as new techniques emerge.

7.4.  Asset level signaling

   Inference is becoming increasingly local (e.g., self-hosted models or
   on-device processing).  Asset-level control or signaling could
   intervene with an individual’s device, and there might be attempts to
   ensure a user cannot override a "no inference" flag for personal use
   (like translating a document), which is highly problematic.

8.  Case Studies

8.1.  Inhibition of Not-for-Profit Crawlers

   Non-profit organizations dedicated to archiving and public benefit,
   such as the Common Crawl Foundation, the Internet Archive and Data
   Rescue Project, are often inadvertently blocked or disadvantaged by
   broad control mechanisms intended to stop commercial scraping.

8.2.  Opaque Robot Defenses

   Modern “robot defenses” are increasingly used by websites to stop
   crawling instead of relying solely on the transparent robots.txt
   protocol.  Providers of these defenses sometimes treat legitimate
   archive crawlers, like Common Crawl’s CCBot, the same way as
   commercial bots crawling for specific AI companies.  This blocking
   process is considered opaque.

8.3.  Collateral Damage to Beneficial Uses

   This conflation of uses often leads to "collateral damage" for non-AI
   uses, effectively "locking down the Web".  For example, the official
   2024 End of Term Archive has been inadvertently blocked from crawling
   some US government websites due to these defensive measures.










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8.4.  Threat to Public Archiving

   Organizations providing vital public benefit, such as the Internet
   Archive's Wayback Machine, are constantly under threat because courts
   sometimes impose a duty to parse individualized, idiosyncratic Terms
   of Service (ToS) for every archived page.  Such enforcement would
   make the vital historical, academic, legal, and journalistic work
   provided by the Wayback Machine impossible.

8.5.  Intermediaries Choosing for Smaller Site Operators

   A significant problem arising from this overreach is that creators
   and smaller site operators who wish to share their content for uses
   like AI training, or search indexing are often prevented from doing
   so because the hosting platform or site administrator chooses a
   restrictive standard for them by default.

8.6.  Platform Control over Creator Intent

   Public websites, particularly those hosting user-generated content
   (UGC) like social media platforms, use broad Terms of Service (ToS)
   to assert extensive, contract-based control over content and data.
   If a site has content from many creators (e.g., social media
   platforms), the site administrator typically controls the location-
   based opt-out mechanisms (like robots.txt).  Consequently, the
   administrator "may not allow them [individual creators] to express
   their preferences fully, or at all".

8.7.  Creation of Quasi-IP Rights

   Platforms use their ToS to create quasi-intellectual property rights
   intended to bind the public and impose constraints that stifle
   academic research and preservation, even over content they do not own
   the copyright to.

8.8.  The Threat of Exclusionary Defaults

   When platforms implement restrictive measures (even if intended to be
   protective), they lock away valuable resources that could otherwise
   help the open web.  For instance, a valuable public-interest
   resource, such as a digital dictionary, could be blocked from all AI
   systems by the organizations hosting it, resulting in the resource
   being inaccessible to speakers, researchers, and language models that
   could otherwise preserve and revitalize the language.







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9.  The Impact on the Internet

   Using Internet Society Internet Impact Assessment tool, we have done
   a brief Internet impact assessment on several concepts we have been
   discussing during the working group dialogues:

     +====================+=========================================+
     | Critical Property  | Potential Impact                        |
     | / Goal             |                                         |
     +====================+=========================================+
     | *CP1 – Accessible  | All-or-nothing AI-pref signals increase |
     | infrastructure*    | participation costs, especially for     |
     |                    | small/open-source actors.               |
     +--------------------+-----------------------------------------+
     | *CP2 –             | Broad bans reduce modular reuse (e.g.,  |
     | Interoperable      | summarization/translation), stifling    |
     | building blocks*   | recombination.                          |
     +--------------------+-----------------------------------------+
     | *CP3 –             | Platform-wide defaults override creator |
     | Decentralized      | intent; reduce autonomy in UGC          |
     | management*        | contexts.                               |
     +--------------------+-----------------------------------------+
     | *CP4 – Common      | No impact identified yet.               |
     | global             |                                         |
     | identifiers*       |                                         |
     +--------------------+-----------------------------------------+
     | *CP5 – Technology- | Banning “AI” as a category risks        |
     | neutral network*   | chilling general-purpose uses (e.g.,    |
     |                    | accessibility).                         |
     +--------------------+-----------------------------------------+

                                  Figure 1

10.  Proposed End User Impact Principles

   To the greatest extent possible, the goal should be to keep public
   information public and to keep the open web open.  Vocabulary
   standardized by IETF should not unduly restrict end user access to
   and ability to use public knowledge and information.  Definitions
   should be clear and narrowly tailored to be fit for purpose, rather
   than vague and broad.  End users should be able to understand the
   definitions without having technical or legal expertise.  End users
   should be able to use general purpose, commercially available tools
   to accomplish their own lawful activities, regardless of preferences
   to the contrary.






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11.  Conclusion

   AI Preference signaling, while conceived as a mechanism for
   expressing control and transparency over the use of online content,
   risks evolving into an instrument that fragments the open Internet
   and uses of information.  The IETF’s role has never been to protect
   business models from innovation or to enforce intellectual property
   regimes.  Its role is to ensure interoperability and resilience
   across technical systems.  When preference signaling becomes a
   vehicle for implementing legal or economic restrictions at the
   protocol layer, it moves the IETF into a domain of governance that
   undermines its core mission and values.

   End users, whether researchers, accessibility developers, educators,
   or ordinary participants in the digital commons, stand to lose most
   from these shifts.  Overly restrictive signaling can chill legitimate
   research, disable accessibility tools, and entrench barriers to
   knowledge that the Internet was designed to dismantle.  By conflating
   technical coordination with policy enforcement, we risk replacing
   openness and innovation with exclusion and opacity.

   The AI-Pref Working Group should therefore focus on preserving
   interoperability and user agency, ensuring that standards remain
   technology-neutral, modular, and open to innovation.

12.  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.

13.  Security Considerations

   TODO Security

14.  IANA Considerations

   This document has no IANA actions.

15.  Normative References

   [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/info/rfc2119>.




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   [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/info/rfc8174>.

Acknowledgments

   TODO acknowledge.

Authors' Addresses

   Farzaneh Badii
   Digital Medusa
   Email: farzaneh@digitalmedusa.org


   Lila Bailey
   Internet Archive
   Email: lila@archive.org


   Jo Levy
   Email: jlevy@nortonlaw.com





























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