Global Divergence in AI Copyright Liability: A
Comparative Analysis of Fair Use, Text and Data Mining, and Fair Dealing in the
US, EU, and Caribbean (2025),
By. Dr. Abiola Inniss Ph.D. LLM, ACIarb
Abstract
This article analyzes the diverging legal frameworks
governing AI training and copyright in the United States, European Union, and
Caribbean as of late 2025. In the United States, the judiciary has
established a “conditional fair use” doctrine (Bartz v. Anthropic,
Kadrey v. Meta), where training is transformative but liability arises
from illicit data sources (“shadow libraries”) or market
substitution. The European Union enforces a statutory compliance regime
under the AI Act, permitting text and data mining (TDM) only where rights
holders have not exercised machine-readable opt-outs (e.g., C2PA). In the Caribbean,
notably Barbados, legislative reforms prioritize creator sovereignty, rejecting
broad TDM exceptions in favor of collective licensing. The analysis concludes
that global AI liability is fractured: permitted as fair use in the US (with
caveats), conditional on protocols in the EU, and largely infringing in the
Caribbean, creating a complex compliance landscape for global AI development.
Executive Summary
As the global digital economy transitions into late 2025,
the legal frameworks governing the intersection of Artificial Intelligence (AI)
training and copyright law have fractured into distinct, often competing,
regulatory paradigms. This report offers an exhaustive analysis of the
judicial, legislative, and regulatory developments in the United States, the
European Union, and the Caribbean regarding the legality of ingesting
copyrighted works for the purpose of training generative AI models.
In the United States, 2025 has been a year of
judicial refinement rather than legislative overhaul. The U.S. Copyright
Office, in its pivotal Part 3 Report, declined to recommend a statutory blanket
license, leaving the courts to define the boundaries of Section 107 “Fair
Use”.1 The resulting case law, specifically the summary
judgment rulings in Bartz v. Anthropic and Kadrey v. Meta, has
established a “bifurcated” liability model. While the act of
intermediate copying for the purpose of pattern recognition is generally viewed
as transformative and fair, this defense collapses if the underlying data
source is illicit (e.g., “shadow libraries”). Thus, US liability is
increasingly defined by data provenance and output substitution
rather than the training process itself.23
In the European Union, the focus has shifted from
judicial interpretation to strict regulatory compliance following the full
implementation of the AI Act in August 2025. The EU regime is predicated on a
statutory “Text and Data Mining” (TDM) exception under the Digital
Single Market (DSM) Directive, which is heavily conditioned on a
“machine-readable opt-out” mechanism. The legal battleground in
Europe has moved to the technical layer, with German courts ruling in late 2025
that AI training is permitted under TDM exceptions unless rights holders
have strictly adhered to emerging technical protocols like C2PA and TDM
Reservation Protocols, as identified by the EUIPO.45
In the Caribbean, the region is navigating a complex
transition between colonial-era “Fair Dealing” statutes and the
urgent need for digital sovereignty. Barbados has emerged as the
legislative vanguard, passing the Copyright Bill, 2025 to empower
Collective Management Organizations (CMOs) and extend protections, signaling a
pro-creator stance that implicitly rejects a broad US-style fair use for AI.67
Conversely, Jamaica and Trinidad and Tobago have focused on
judicial regulation, issuing Practice Directions to curb AI
“hallucinations” in court while retaining conservative copyright
frameworks that likely classify unlicensed commercial AI training as
infringement.89
This article explores these diverging paths, analyzing the
economic and legal ripple effects of a world where data training is “fair
use” in California, a “conditional exception” in Berlin, and
potentially “infringement” in Bridgetown.
1. The United States: The Judicial Refinement of Fair Use
The United States legal system, relying on the flexibility
of common law, spent 2025 rigorously testing the applicability of the
“Fair Use” doctrine (17 U.S.C. § 107) to the mass ingestion of
copyrighted works. Unlike other jurisdictions that sought statutory fixes, the
US approach has been defined by high-stakes litigation in federal courts,
primarily the Northern District of California and the Southern District of New
York.
1.1 The Policy Baseline: The US Copyright Office Part 3
Report
On May 9, 2025, the U.S. Copyright Office released its
long-awaited report, Copyright and Artificial Intelligence: Part 3 –
Generative AI Training. This document serves as the foundational policy
text for the US government’s stance on AI and copyright in 2025.1
The Report effectively ended speculation regarding an
immediate legislative “fix” such as a compulsory licensing scheme.
Instead, the Office reaffirmed that existing copyright principles, specifically
the fair use doctrine, should govern the training of AI models. However, the
Office’s analysis was far from a blanket endorsement of the AI industry’s
practices. The Report emphasized that fair use is a context-specific,
fact-intensive inquiry that cannot be determined in the abstract.1
Crucially, the Report questioned the
“transformativeness” of generative AI models in instances where the
model’s purpose is to generate content that competes directly with the training
data. The Office noted that while the technical act of copying for analysis
(intermediate copying) has historically been protected, the “purpose and
character” analysis for Generative AI is complicated by the fact that the
output often serves the same intrinsic purpose as the input—creative
expression.1 This “market substitution” concern became the
central theme of subsequent litigation.
1.2 The “Bifurcated” Fair Use Doctrine: Bartz
and Kadrey
The most significant legal development of 2025 was the
emergence of a “bifurcated” fair use analysis, crystallized by two
summary judgment rulings in June 2025: Bartz v. Anthropic PBC and Kadrey
v. Meta Platforms, Inc. These cases dismantled the monolithic view of
“AI training” and instead separated the act of training from
the source of the data.
1.2.1 Bartz v. Anthropic: The Piracy Exception
In Bartz v. Anthropic, Judge William Alsup of the
Northern District of California addressed whether Anthropic’s use of
copyrighted books to train its Claude models constituted fair use. The
plaintiffs alleged that Anthropic utilized “The Pile,” a massive
dataset that included “Books3,” a constituent dataset sourced from
“shadow libraries” (pirated ebook repositories like Bibliotik).2
The court’s ruling on June 23, 2025, was nuanced. Judge
Alsup agreed with Anthropic on the technical aspect of training, finding that
the computational analysis of texts to extract statistical correlations and
linguistic patterns was “highly transformative.” This use served a
fundamentally different purpose than the original books (which were meant for
human entertainment), thus satisfying the first factor of fair use regarding
the training process itself.10
However, the court denied summary judgment on the issue of
the data source. Judge Alsup ruled that “Anthropic had no entitlement to
use pirated copies for its central library” and that “Creating a
permanent, general-purpose library was not itself a fair use excusing
Anthropic’s piracy”.2 This ruling established a critical
precedent: Fair use cannot cleanse the taint of piracy. Even if the use
(training) is transformative, the possession and ingestion of
illicitly acquired data remains an infringing act. This decision forced AI
developers across the US to audit their datasets and purge components derived
from shadow libraries to avoid liability.2
1.2.2 Kadrey v. Meta: The “Market
Competition” Theory
Two days later, on June 25, 2025, Judge Vince Chhabria
issued a ruling in Kadrey v. Meta Platforms, Inc. that further tightened
the fair use defense. Similar to Bartz, the plaintiffs (authors) alleged
that Meta trained its LLaMA models on copyrighted books without permission.
Meta argued that its use was transformative and therefore fair use as a matter
of law.11
While acknowledging the transformative nature of extracting
information from books to build a language model, Judge Chhabria introduced a
“market competition” theory that expanded potential liability. The
court denied Meta’s motion for total exoneration, explicitly noting that
“fair use… typically doesn’t apply to copying that will significantly
diminish the ability of copyright holders to make money from their works”.11
The court emphasized that if an AI model creates a
“substitute” for the original work, the transformative defense is
weakened. This ruling shifted the burden of proof onto AI companies to
demonstrate that their models do not negatively impact the market for the
original works—a difficult bar to clear given the capability of LLMs to
generate summaries, sequels, or stylistically similar prose that could
theoretically displace the original author.11
1.3 The “Substitution” Frontier: NYT v.
OpenAI and Perplexity
Following the precedents set in the summer of 2025, the
legal focus in late 2025 shifted from the training phase to the output phase,
specifically regarding news and non-fiction content where the risk of
substitution is acute.
1.3.1 The New York Times v. OpenAI
As of December 2025, this case remains in a contentious
discovery phase. The core allegation is that ChatGPT regurgitates New York
Times articles verbatim, serving as a direct market substitute for the
newspaper’s subscription service. In a significant development in late 2025,
Judge Ona Wang ordered OpenAI to produce 20 million chat logs.12
This discovery order aims to empirically test the “regurgitation”
rate of the model. If the plaintiffs can prove that the model consistently
outputs substantial portions of copyrighted articles, the “fair use”
defense likely fails under the fourth factor (market effect), regardless of how
transformative the training process was.
1.3.2 The New York Times v. Perplexity
On December 5, 2025, the New York Times opened a new
front in the AI copyright war by suing Perplexity AI.13 This lawsuit
differs from the OpenAI case as it targets “Retrieval-Augmented
Generation” (RAG) rather than just model training. The Times
alleges that Perplexity’s search engine creates “substitutes” for its
journalism by scraping real-time content and summarizing it for users,
bypassing the paywall and depriving the publisher of ad revenue and
subscriptions.13
This case challenges the “fair use” status of RAG
systems. While search engines have historically enjoyed fair use protection
(e.g., Authors Guild v. Google), the Times argues that Perplexity
does not just index content but replaces it. A ruling against Perplexity
could imperil the business models of AI search assistants that rely on
real-time web summarization.13
1.4 Visual Arts and Compressed Copies: Andersen v.
Stability AI
In the realm of visual arts, the case of Andersen v.
Stability AI proceeded toward a September 2026 trial date, but significant
rulings in 2025 shaped the landscape.14 The court allowed claims to
proceed regarding “compressed copies,” accepting the plausible theory
that the AI model’s weights might contain compressed representations of the
training images, thereby constituting a derivative work or a form of storage
infringement.14
This theory attacks the “abstraction” defense used
by AI companies (which argues that models only store mathematical patterns, not
images). If plaintiffs can prove that the model can reconstruct the original
training images (memorization), the “fair use” defense for training
becomes significantly harder to maintain, as it resembles storage and
distribution rather than just learning.14
1.5 Synthesis of US Jurisprudence
By the end of 2025, the US legal position on AI training and
fair use can be summarized as Conditional Fair Use:
- Training
Process: The intermediate copying of data for the purpose of pattern
recognition is generally considered transformative and fair use.10 - Data
Source: The use of illicitly acquired data (e.g., shadow
libraries) is infringement and is not shielded by fair use (Bartz).2 - Market
Effect: If the AI model generates outputs that serve as market
substitutes for the original works (e.g., verbatim regurgitation,
detailed summaries), the fair use defense is likely to fail (Kadrey,
NYT).1113
2. The European Union: The Statutory Compliance Regime
While the US navigated the nuances of common law, the
European Union spent 2025 operationalizing a comprehensive statutory framework.
The EU’s approach is characterized by explicit legislative exceptions for
“Text and Data Mining” (TDM) coupled with rigorous transparency and
opt-out obligations mandated by the AI Act.
2.1 The AI Act and the DSM Directive: A Regulatory Pincer
The EU AI Act entered into full force in mid-2025,
with key provisions regarding General-Purpose AI (GPAI) models becoming
applicable on August 2, 2025.15 The AI Act does not create a new
copyright exception; rather, it enforces the existing framework established by
the Directive on Copyright in the Digital Single Market (DSM Directive)
(2019/790).
The interaction between these two laws creates the EU’s
unique “Opt-Out” regime:
- Article
3 (DSM): Provides a mandatory exception for TDM for the purposes of scientific
research by research organizations and cultural heritage institutions.
This exception cannot be overridden by contract or opt-outs.16 - Article
4 (DSM): Provides an exception for TDM for any other purpose
(including commercial AI training), unless the rightsholder has
expressly reserved their rights in an “appropriate manner, such as
machine-readable means”.16
The AI Act bridges the gap by mandating that providers of
GPAI models must (a) put in place a policy to comply with EU copyright law, and
specifically (b) identify and respect the opt-outs exercised under Article 4(3)
of the DSM Directive.17
2.2 The “Machine-Readable” Standard and
Technical Protocols
The critical legal battleground in the EU in 2025 was the
definition of “machine-readable means.” Because Article 4 allows
commercial TDM unless an opt-out is present, the technical validity of
the opt-out determines the legality of the training.
In July 2025, the European Commission published the GPAI
Code of Practice, which established the standards for compliance.
Simultaneously, the EUIPO (European Union Intellectual Property Office)
Study on Generative AI and Copyright provided the technical blueprint for
these standards.4
The EUIPO study and the subsequent consultation identified a
hierarchy of protocols:
- Robots
Exclusion Protocol (robots.txt): While widely used, the study noted
its limitations (lack of granularity). However, the GPAI Code of Practice
explicitly requires signatories to respect robots.txt as a baseline.17 - TDM
Reservation Protocol (TDM Rep): A W3C community group specification
designed specifically for Article 4 compliance, allowing granular rights
reservation at the file level.4 - C2PA
(Coalition for Content Provenance and Authenticity): A cryptographic
standard for asserting provenance and rights, endorsed as a
“state-of-the-art” method for expressing opt-outs in media
files.4 - Rights
Reservation Databases: The study proposed “federated rights
reservation databases” (e.g., via the Liccium Trust Engine or
Valuenode) to allow rights holders to register opt-outs centrally,
addressing the difficulty AI crawlers face in parsing every individual
website.4
Implication: In 2025, an EU rightsholder who merely
states “No AI Training” in a footer or Terms of Service without
implementing a machine-readable protocol (like robots.txt or C2PA metadata) has
arguably failed to validly opt-out under Article 4(3). Consequently,
scraping their content for AI training would be legal under the TDM exception.45
2.3 Judicial Validation: Germany as the Testbed
Germany, often the bellwether for EU copyright
jurisprudence, provided the first major court rulings interpreting these
provisions in late 2025.
2.3.1 Kneschke v. LAION (Hamburg Regional Court,
September 2025)
In this landmark case, a photographer sued LAION (a
non-profit that compiles datasets for AI training) for including his images in
the LAION-5B dataset. The Hamburg Regional Court ruled in favor of LAION,
finding that the creation of the dataset qualified as TDM for scientific
research under Section 60d of the German Copyright Act (which implements
Article 3 of the DSM Directive).18
Crucially, the court held that the creation of a
dataset for AI training is intrinsically an act of “text and data
mining.” This validated the legal basis for non-profit research
organizations to aggregate data for AI development without needing licenses,
provided they fall under the scientific research definition.18
2.3.2 GEMA v. OpenAI (Munich Regional Court,
November 2025)
In November 2025, the Munich Regional Court issued a ruling
in GEMA v. OpenAI. GEMA, the German music collecting society, argued
that OpenAI’s use of lyrics constituted infringement. The court affirmed that
the TDM exception (Article 4) generally covers the ingestion of data for AI
training.19
However, the court introduced a critical caveat: Memorization
equals Infringement. The court ruled that if the AI model is capable of
reproducing the training data (e.g., outputting full song lyrics), it has
exceeded the scope of the TDM exception. TDM allows for the extraction
of patterns, not the reproduction of the work itself. Therefore,
“overfitting” (where a model memorizes its training data) strips the
developer of the TDM protection, rendering them liable for reproduction
infringement.19
2.4 Synthesis of EU Jurisprudence
By late 2025, the EU position is a Statutory Opt-Out
Regime:
- Commercial
Training: Permitted by default under Article 4 DSM, provided
the developer respects machine-readable opt-outs (robots.txt, C2PA).517 - Scientific
Research: Permitted unconditionally under Article 3 DSM for qualifying
institutions.16 - Liability
Trigger: Liability attaches if the developer ignores valid opt-outs OR
if the model generates infringing outputs (memorization/reproduction).19 - Penalty:
Violations are subject to the AI Act’s massive fines (up to 7% of global
turnover) and copyright damages.15
3. The Caribbean: Fair Dealing and Digital Sovereignty
In the Caribbean, the legal landscape in 2025 is marked by a
tension between the legacy of colonial “Fair Dealing” laws and the
modern necessity of digital sovereignty. Unlike the US “fair use”
(which is open-ended and flexible) or the EU “TDM exception” (which
is specific and statutory), Caribbean copyright laws generally require specific
statutory exceptions for any use that is not authorized by the copyright
holder.
3.1 The Regional Context: Fair Dealing vs. Fair Use
Most Caribbean jurisdictions (including Barbados, Jamaica,
and Trinidad & Tobago) operate under a “Fair Dealing” framework
derived from UK law. Fair dealing is traditionally limited to specific
purposes: research, private study, criticism, review, and news reporting. It
does not typically include a broad “transformativeness” test, nor
does it automatically extend to commercial data processing.20
In 2025, the lack of a specific “Text and Data
Mining” exception in most Caribbean laws means that commercial AI training
likely falls outside the scope of fair dealing, rendering it potentially
infringing absent a license.20
3.2 Barbados: The Legislative Vanguard
Barbados distinguished itself in 2025 by passing significant
copyright reform, positioning itself as a leader in protecting creator rights
in the digital age.
3.2.1 The Copyright Bill, 2025
Passed in October 2025, the Copyright Bill, 2025
replaced the outdated 1998 legislation. The bill was explicitly designed to
address “digital content and streaming” and to “strengthen
penalties for infringement”.621
Key provisions impacting AI and data rights include:
- Empowerment
of CMOs: The Act formally empowers Collective Management Organizations
(like COSCAP) to take direct legal action against infringers on behalf of
their members. Previously, CMOs lacked the locus standi to sue,
leaving individual creators to fight costly legal battles. This change
significantly increases the litigation risk for AI companies that might
ingest Barbadian music or literature without a license.621 - Extension
of Term: The Act extends copyright protection by 20 years, aligning
with international trends and ensuring a longer period of exclusivity for
rights holders.21 - No
TDM Exception: Notably, despite global trends, the Bill did not
introduce a broad fair use or TDM exception for AI training. By
strengthening enforcement without creating an AI loophole, Barbados has
effectively signaled a “permission-first” regime. AI
developers wishing to train on Barbadian works (e.g., the extensive
catalog of soca and calypso) must negotiate licenses.20
3.3 Jamaica: Judicial Regulation and
“Originality”
Jamaica has focused its 2025 regulatory efforts on the application
of AI in the justice system rather than amending copyright law to facilitate AI
development.
3.3.1 Practice Directions and “Hallucinations”
Chief Justice Bryan Sykes issued the Practice Direction
on the Use of Artificial Intelligence in 2025. This regulation strictly
prohibits the use of AI to draft affidavits, witness statements, or expert
evidence to prevent the submission of “hallucinations” (fabricated
case law or facts) to the court. While this regulates the legal profession, it
reflects a broader judicial skepticism toward generative AI’s reliability.8
3.3.2 Copyright Law and Originality
Jamaica continues to operate under the Copyright Act
with traditional fair dealing exceptions (Sections 52-55) limited to
“research or private study”.22
A critical jurisprudential barrier to AI in Jamaica is the
“originality” requirement. The Jamaican Supreme Court, in Cabel
Stephenson v. Doreen Hibbert, reiterated that originality requires
“skill and labour” from a human author.23 This
precedent implies that:
- Inputs:
Training on Jamaican works for commercial purposes is not “private
study” and is likely infringement. - Outputs:
AI-generated works are likely not copyrightable in Jamaica, as they lack
the requisite human “skill and labour”.23
Additionally, Jamaica amended its Copyright Act to accede to
the Marrakesh Treaty (facilitating access for the blind), showing a
willingness to create humanitarian exceptions, but has notably refrained from
creating commercial AI exceptions.24
3.4 Trinidad and Tobago: The “Human Author”
Stance
Trinidad and Tobago remains the most conservative of the
three jurisdictions regarding AI copyright reform.
- Legislative
Status: The Copyright Act contains no express exception for
text and data mining or AI training. Section 24 lists specific exceptions,
none of which broadly cover machine learning.25 - Judicial
Rulings: In May 2025, Justice Westmin James sanctioned attorneys for
submitting AI-generated fictitious case law, reinforcing the judiciary’s
view of AI as a tool requiring strict human oversight.9 - Policy
Stance: The prevailing legal consensus, supported by the
UNESCO/CARICOM AI Policy Roadmap, is that the region must “safeguard
intellectual property rights” rather than dilute them. Legal experts
in Port of Spain emphasize that a “human hand” is required for
copyright, and that the mass scraping of data for commercial gain
infringes on the moral and economic rights of creators.26
3.5 Regional Oversight: The Caribbean Court of Justice
(CCJ)
The CCJ, the final appellate court for Barbados and other
CARICOM states, issued Practice Direction No. 1 of 2025 regarding
Generative AI. This direction mandates that any use of AI in court proceedings
must be disclosed and verified. This high-level judicial oversight reinforces a
regional norm: AI is a high-risk tool that must be subordinated to human
verification and legal accountability, a stance that aligns with a restrictive
view of AI’s copyright privileges.27
4. Comparative Analysis: The “Compliance
Fracture”
The divergence in legal frameworks across these three
regions creates a complex global compliance landscape for AI developers. The
following table illustrates the “Compliance Fracture” as of December
2025.
Table 1: Comparative Liability for AI Training (Dec 2025)
|
Feature |
United |
European |
Caribbean |
|
Primary |
Fair |
TDM |
Fair |
|
Legality |
Likely |
Permitted by default, unless |
Likely |
|
Legality |
Infringement. Fair use does not apply to |
Infringement. Requirement for “lawfully |
Infringement. |
|
Opt-Out |
Voluntary. No legal obligation to respect |
Mandatory. Must respect machine-readable |
N/A. Permission is required by |
|
Liability |
Provenance |
Process |
Authorization (Did you obtain a license from |
|
2025 |
Bartz & Kadrey bifurcated |
Full |
Barbados |
4.2 Second-Order Insight: The “Data Laundering”
Risk and Regulatory Arbitrage
The stark difference between the EU’s “Opt-Out”
regime and the US “Conditional Fair Use” creates a significant risk
of regulatory arbitrage.
- The
“Havana” Harbor: AI developers might be tempted to move
training operations to jurisdictions with broad TDM exceptions (like
Japan, Singapore, or potentially Israel) to avoid the EU’s strict
transparency requirements or the US’s litigation risks.20 - The
“Shadow Library” Trap: However, the US ruling in Bartz
acts as a global restraint. Since the court ruled that the possession
of an illicit dataset (like Books3) negates fair use, a company cannot
simply “wash” pirated data in a permissive jurisdiction and then
deploy the model in the US. The “fruit of the poisonous tree”
doctrine effectively extends US copyright standards to the global supply
chain of any AI model intended for the American market.2
4.3 The “North-South” Divide in AI Copyright
The Caribbean’s resistance to adopting US-style fair use or
EU-style TDM exceptions highlights a growing “North-South” divide.
- The
Global North (US/EU): Generally favors exceptions that allow their
massive tech industries to ingest data at scale, viewing data as a
“raw material” for innovation. - The
Global South (Caribbean): Views their cultural output (music,
literature, art) as a primary economic asset. By rejecting TDM exceptions
and strengthening CMOs (as Barbados did), these nations are asserting that
their data is a “finished product” requiring compensation.621 - Economic
Implication: This creates a barrier to entry for local Caribbean AI
startups, who cannot rely on “fair use” to train models on local
content. They face high transaction costs (licensing) that their Silicon
Valley competitors (who might ignore Caribbean jurisdiction or claim fair
use in the US) do not. This could ironically entrench the dominance of
foreign AI models in the Caribbean region.
5. Future Outlook (2026 and Beyond)
As we look toward 2026, the legal conflict is evolving from
“Can we train?” to “What can we output?”
- The
“Substitution” War: The filing of NYT v. Perplexity
in late 2025 signals that the next great legal battle will be over Retrieval-Augmented
Generation (RAG). If courts rule that summarizing real-time news is
“substitution” and not “fair use,” the entire business
model of AI search could collapse or be forced into a licensing model.13 - Technical
Standardization: In the EU, the focus will be on the adoption of the TDM
Reservation Protocol and C2PA. We expect 2026 to be the year
where “machine-readable rights” become a standard part of file
metadata, forcing a technological overhaul of how content is published on
the web.417 - Caribbean
Licensing Markets: With Barbados empowering CMOs, we expect to see the
first test cases of Caribbean collective management organizations
demanding royalties from global AI platforms for the use of Caribbean
music and literature. This could lead to new cross-border licensing
agreements or, alternatively, the “geoblocking” of Caribbean
content from AI training sets.6
6. Conclusion
In 2025, the answer to “Does AI training constitute
Fair Use?” depends entirely on where the server—and the lawyer—is located.
- In
the US: Yes, it is Fair Use, if you didn’t steal the data and
your AI doesn’t plagiarize the output. The courts have carved out a space
for innovation but fenced it off from piracy and direct market
competition.1011 - In
the EU: Yes, it is a TDM Exception, if you respect the
technical “No Trespassing” signs (opt-outs) and follow the
bureaucratic map of the AI Act.5 - In
the Caribbean: No, it is likely Infringement. The region has chosen to
prioritize the economic rights of its creators over the unbridled
expansion of AI training, betting that strong copyright enforcement will
ultimately yield better economic returns than permissive open-ended
exceptions.6
The “Fair Use” consensus of the early 2020s has
shattered. In its place is a complex, fragmented global map of compliance,
where data provenance, technical protocols, and jurisdictional boundaries
define the legality of artificial intelligence.
7. References
|
Ref |
Title |
Description |
|
1 |
US |
Copyright |
|
2 |
Bartz |
Bartz |
|
3 |
Bartz |
Settlement |
|
4 |
EUIPO |
Study |
|
5 |
EU |
European |
|
6 |
Barbados |
Copyright |
|
7 |
Barbados |
Confirmation |
|
8 |
Jamaica |
Practice |
|
9 |
Trinidad |
Justice |
|
10 |
Bartz |
Ruling |
|
11 |
Kadrey |
Kadrey |
|
12 |
NYT |
Order |
|
13 |
NYT |
The |
|
14 |
Andersen |
Andersen |
|
15 |
EU |
Implementation |
|
16 |
EU |
Analysis |
|
17 |
GPAI |
EU Code |
|
18 |
Kneschke |
Kneschke |
|
19 |
GEMA |
GEMA |
|
20 |
Caribbean |
Analysis |
|
21 |
Barbados |
Legislative |
|
22 |
Jamaica |
Sections |
|
23 |
Jamaica |
Cabel |
|
24 |
Jamaica |
Amendments |
|
25 |
Trinidad |
Copyright |
|
26 |
Caribbean |
UNESCO/CARICOM |
|
27 |
CCJ |
Practice |
Works cited
- Copyright
and Artificial Intelligence | U.S. Copyright Office, accessed December 5,
2025, https://www.copyright.gov/ai/ - Copyright
Office Issues Key Guidance on Fair Use in Generative AI Training – Wiley
Rein, accessed December 5, 2025, https://www.wiley.law/alert-Copyright-Office-Issues-Key-Guidance-on-Fair-Use-in-Generative-AI-Training - Two
California District Judges Rule That Using Books to Train AI is Fair Use,
accessed December 5, 2025, https://www.whitecase.com/insight-alert/two-california-district-judges-rule-using-books-train-ai-fair-use - Fair
Use and AI Training: Two Recent Decisions Highlight the Complexity of This
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