Israel - Copyright Use for Machine Learning

Office of Legal Counsel and Legislative Affairs (Ministry of Justice) opinion on the use of copyrighted works for machine‑learning purposes

Israel

RAI-IL-NA-OLCLAXX-2022
In Force(In Force)As published at gov.il

Israel - Copyright Use for Machine Learning is In Force in Israel, according to gov.il. We have not yet been able to confirm the status.

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On 18 December 2022 the Office of Legal Counsel and Legislative Affairs at Israel’s Ministry of Justice published a non‑binding legal opinion concluding that, in general and subject to caveats, the use of copyrighted works to train machine‑learning systems is permissible under existing provisions of the Israeli Copyright Act (notably fair use, incidental use and transient/ephemeral use). The opinion applies to the training/process stage (not to downstream outputs) and highlights exceptions where permission remains necessary.

Summary

On 18 December 2022 the Office of Legal Counsel and Legislative Affairs (Ministry of Justice, State of Israel) issued a non‑binding legal opinion titled 'Uses of Copyrighted Materials for Machine Learning' that analyzes whether machine‑learning (ML) enterprises may lawfully use copyrighted works to train AI systems without rights‑holder authorization. The opinion interprets the Israeli Copyright Act (2007) and concludes that, except in particular circumstances, the creation and use of ML training databases that include copyrighted material will typically be lawful under existing statutory exceptions: (1) fair use (Section 19), (2) incidental use (Section 22), and (3) transient or ephemeral reproduction (Section 26). The opinion frames ML training as research/study and a transformative process for many use cases; it therefore often meets the fairness factors (purpose and character, nature of the work, amount and substantiality of the portion used, and market effect). It also explains that incidental‑use and transient‑copy doctrines can apply when works are used as mere means to enable learning and are deleted once training completes.

The opinion is careful to limit its scope. It expressly disclaims a categorical safe harbor for ML outputs: the legality of model outputs must be assessed independently and may infringe copyright even where training was lawful. The opinion also excludes datasets composed predominantly or exclusively of the works of a single creator intended to produce outputs that compete directly with that creator’s market — such uses are unlikely to qualify for the cited statutory defenses. The Ministry further notes that contractual terms (e.g., terms of service forbidding scraping/mining) may be ineffective where they constitute unconscionable standard‑form terms, but lawful access and avoidance of technological circumvention (e.g., bypassing paywalls) remain important.

Although non‑binding, the opinion aligns Israel with an international trend toward recognizing fair‑use/TDM exceptions for research and machine learning, and it aims to reduce legal uncertainty to spur innovation while flagging rights‑holder and market‑impact risks. Practical takeaways recommended include conducting fair‑use analyses, documenting dataset provenance and lawful access, minimizing retained copies (delete when training completes), assessing market effects and single‑author risks, and separately assessing model outputs for infringement. The opinion is best read as guidance for courts, rights‑holders, researchers and companies rather than as an amendment to statute; it provides policy reasoning and legal interpretation but leaves case‑by‑case adjudication to the judiciary.

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Overview

The Office of Legal Counsel and Legislative Affairs at the Ministry of Justice published an interpretive legal opinion on 18 December 2022 addressing whether and when the use of copyrighted works for machine‑learning (ML) training is lawful under Israeli copyright law. The opinion — available from the Ministry — analyzes three statutory exceptions of the Copyright Act of 2007 (fair use, incidental use and transient/ephemeral copy) and concludes that, in most ordinary ML scenarios, creating and using datasets that include copyrighted materials for training will be permissible, subject to important limits. It emphasises that the opinion applies to the learning process itself and not automatically to model outputs, and aims to reduce legal uncertainty for Israeli R&D and commercial AI actors while balancing creators’ interests. See the full text: Ministry of Justice — Uses of Copyrighted Materials for Machine Learning (Hebrew, 18 Dec 2022).

Definitions

Key terms used in the opinion include: "machine learning" (automated training of algorithms from data), "training database/dataset" (collections of works used to feed ML models), "fair use" (Section 19 of the Copyright Act permitting certain uses for study/research etc.), "incidental use" (Section 22 permitting uses where a work appears as an incidental element of another work or process) and "transient/ephemeral use" (Section 26 permitting temporary technical copies that have no independent economic value). The opinion distinguishes between the training process (ingestion, copying and internal representation) and downstream outputs (generated text, images, audio) and treats them as legally distinct questions.

Governance and Institutional Framework

The opinion was issued by the Ministry’s Office of Legal Counsel and Legislative Affairs — the body responsible for legal and legislative interpretation and government legal advice. It is non‑binding guidance rather than primary legislation. The Ministry situates its view within Israel’s copyright statutory framework and references comparative approaches (e.g., US fair use jurisprudence, EU TDM exceptions and policy positions in the UK, Japan and Singapore). While only courts can issue authoritative judicial rulings, the opinion is intended to guide government policy, public agencies and private sector actors and to inform judicial reasoning in future disputes. The opinion also intersects with broader national AI policy documents; for example, Israel’s national AI policy discussions reference aligned regulatory and ethical goals and cross‑ministerial coordination. For the Ministry opinion text see official PDF (Hebrew) and related AI policy materials at Israel's AI Policy 2023 (English).

Key Focus Areas

The opinion focuses on several core legal and policy questions: (1) Whether creation and use of datasets containing copyrighted works for ML training infringes exclusive rights; (2) Which statutory exceptions, if any, apply; (3) Whether contractual prohibitions or technical access restrictions can bar lawful uses; (4) Whether the opinion protects model outputs; and (5) Practical limits and high‑risk scenarios. The Ministry concludes that the three doctrines (fair use, incidental use, transient use) will typically permit ML training: fair use as the principal doctrinal basis (characterizing training as study/research and often transformative); incidental use where works function as means to a technological procedure; and transient use where copies are genuinely ephemeral and deleted after training. Important policy caveats include the need to evaluate market effects (fourth factor), the risk that concentrated datasets built to replicate or compete with a single author’s output will not be protected, and the separate legal assessment required for outputs that may reproduce copyrighted expression. The opinion also cautions that circumvention of technological protection or unlawful access to paywalled content remains problematic, and that contractual clauses may be ineffective only when they are unconscionable standard terms; otherwise, lawful access and contract law remain relevant.

Implementation Framework

Although the opinion is not regulatory text, it recommends a practical compliance approach for ML practitioners: adopt a documented fair‑use analysis for each project; ensure lawful access or licensing where required; design datasets to be diverse and not dominated by a single author where competition risks arise; minimize retention of copies and delete training copies once purpose is served to rely on transient‑use protection; keep logs and provenance metadata for dataset sources; implement reasonable security and access controls; and avoid technological circumvention of paywalls or other access restrictions. The Ministry also highlights that separate legal review is required for model outputs, particularly where outputs might reproduce or be substantially similar to protected works or where outputs target the original author’s market.

Monitoring and Evaluation

The opinion encourages monitoring of market effects and case law developments. It recommends that public agencies, the creative industries and ML developers document use patterns, disputed cases and economic impacts to enable future evaluation and, if necessary, legislative refinement. While immediate regulatory monitoring responsibilities are not imposed by the opinion, it signals the Ministry’s intent to track the legal landscape and to coordinate with other government bodies engaged in AI policy. Practitioners are advised to maintain records to support fair‑use determinations and to enable rapid compliance audits if disputes arise.

Penalties, Liability, and Appeals

As interpretive guidance (not statute), the opinion does not itself create penalties. It reiterates that infringing uses remain subject to remedies under the Copyright Act, including civil injunctive relief, statutory and actual damages, and, in willful cases, criminal sanctions available under Israeli law. The opinion clarifies that reliance on statutory exceptions is a defense to infringement claims where the facts support it; where disputes remain, the courts will decide. Affected parties retain statutory and constitutional rights to appeal judicial determinations. Entities relying on the opinion should still apply standard risk management and consider insurance or licensing where infringement risk is material.

Relationship to Other Instruments

The opinion intersects with Israel’s broader AI policy and with contract, data protection and consumer‑protection regimes. It complements rather than supersedes the Copyright Act (2007) and should be read alongside sectoral rules (e.g., privacy laws when datasets include personal data), contract law (service terms and license agreements), and national AI policy documents. The opinion references comparative international instruments (EU TDM exceptions, US fair use jurisprudence) and is consistent with international movement toward legal clarity for text and data mining. It does not amend existing statutes; rather, it provides an official interpretive stance to be considered by courts and practitioners.

International Alignment

The Ministry’s analysis places Israel closer to jurisdictions that use open‑ended fair‑use frameworks (e.g., United States) while noting EU member states have adopted specific TDM exceptions and other jurisdictions (UK, Japan, Singapore) have taken varied approaches. The opinion cites international case law and policy reasoning to justify its interpretation and presents Israel’s stance as consistent with a global trend favoring permissive treatment of research‑oriented ML training, subject to market‑impact safeguards. The document therefore serves both domestic legal clarity and international policy alignment. For comparative reading see analyses published by law firms and commentators and the Ministry’s AI policy paper at Israel's AI Policy 2023.

Implementation Timeline

EventDate
Opinion published by Office of Legal Counsel and Legislative Affairs2022-12-18
Public commentary and legal analyses appeared in Israeli and international pressDec 2022 – Jan 2023
Reference in subsequent national AI policy documents and academic literature2023 onward

Sources and References

SourceType
Office of Legal Counsel and Legislative Affairs, Ministry of Justice — Uses of Copyrighted Materials for Machine Learning (Hebrew, 18 Dec 2022)Primary Source
Israel's AI Policy 2023 (Ministry policy document, English)Primary Source

Requirements for a company

What an organisation has to do under Israel - Copyright Use for Machine Learning, at a glance. Not legal advice — the table below gives the provision and deadline for each item.

Must do

9
  • Ensure lawful access to copyrighted materials or obtain necessary licenses.Machine learning practitioners using copyrighted works for training.
  • Avoid technological circumvention of paywalls or unlawful access to restricted content.Machine learning practitioners acquiring copyrighted works for training.
  • Conduct a separate legal review for model outputs to assess potential infringement.Machine learning practitioners deploying models that generate outputs.
  • Document a fair-use analysis for each machine learning project.Machine learning practitioners using copyrighted works for training.
  • Design datasets to be diverse and not dominated by a single author.Machine learning practitioners creating training datasets.
  • Minimize retention of training copies and delete them once their purpose is served.Machine learning practitioners relying on transient-use protection.
  • +3 more in the table below

Must not do

0

Nothing in this category.

Should do

1
  • Monitor market effects and case law developments related to copyright and machine learning.Machine learning practitioners and public agencies.

Should not do

0

Nothing in this category.

Who must do what

The obligations under Israel - Copyright Use for Machine Learning, most serious first. Not legal advice — verify against the official text before relying on it.

#WhoRequirementBy whenWhereSeverity
1Machine learning practitioners using copyrighted works for training.Ensure lawful access to copyrighted materials or obtain necessary licenses.
“ensure lawful access or licensing where required”
Before acquiring or using copyrighted works for trainingImplementation FrameworkCritical
2Machine learning practitioners acquiring copyrighted works for training.Avoid technological circumvention of paywalls or unlawful access to restricted content.
“circumvention of technological protection or unlawful access to paywalled content remains problematic”
Before acquiring or using copyrighted works for trainingKey Focus AreasCritical
3Machine learning practitioners deploying models that generate outputs.Conduct a separate legal review for model outputs to assess potential infringement.
“separate legal review is required for model outputs, particularly where outputs might reproduce or be substantially similar to protected works”
Before deploying or distributing model outputsImplementation FrameworkCritical
4Machine learning practitioners using copyrighted works for training.Document a fair-use analysis for each machine learning project.
“adopt a documented fair‑use analysis for each project”
Before commencing training with copyrighted worksImplementation FrameworkImportant
5Machine learning practitioners creating training datasets.Design datasets to be diverse and not dominated by a single author.
“design datasets to be diverse and not dominated by a single author where competition risks arise”
Before creating or using training datasetsImplementation FrameworkImportant
6Machine learning practitioners relying on transient-use protection.Minimize retention of training copies and delete them once their purpose is served.
“minimize retention of copies and delete training copies once purpose is served to rely on transient‑use protection”
After training is complete or purpose is servedImplementation FrameworkImportant
7Machine learning practitioners using copyrighted works for training.Maintain logs and provenance metadata for all dataset sources.
“keep logs and provenance metadata for dataset sources”
Continuously, from dataset creationImplementation FrameworkImportant
8Machine learning practitioners managing training datasets.Implement reasonable security and access controls for training datasets.
“implement reasonable security and access controls”
Before using or storing training datasetsImplementation FrameworkImportant
9Entities relying on the opinion for machine learning activities.Apply standard risk management and consider insurance or licensing where infringement risk is material.
“Entities relying on the opinion should still apply standard risk management and consider insurance or licensing where infringement risk is material.”
Continuously, as part of project planning and executionPenalties, Liability, and AppealsImportant
10Machine learning practitioners and public agencies.Monitor market effects and case law developments related to copyright and machine learning.
“The opinion encourages monitoring of market effects and case law developments.”
ContinuouslyMonitoring and EvaluationRecommended

© Regulations.AI · updated on 20 Sep 2026