Precision Contract English for ML Training Data: Warranties on Dataset Provenance and No-Scraping Language

Step 1: Anchor concepts—what a dataset provenance warranty is and how it differs from related terms

A dataset provenance warranty is a contractual promise about where the data came from and how it was obtained. In the context of machine‑learning (ML) training data, it addresses the chain of custody and the legality of acquisition, the authenticity of sources, and the rights the licensor has to grant the licensee. The warranty is about present and past facts: that the licensor has followed lawful, compliant steps and that the dataset does not include content from prohibited or restricted sources (for example, content scraped in violation of terms or law). Because ML projects often combine data from multiple repositories, platforms, and vendors, the provenance warranty is central to reducing downstream legal risk.

It is important to distinguish warranties from related legal terms:

• Representations are statements of fact made to induce the agreement. In US practice, representations often support claims for misrepresentation or fraud if untrue; warranties support breach‑of‑warranty claims. Many contracts combine them as “representations and warranties,” but negotiators should be precise about the consequences of inaccuracy.
• Covenants are promises about future acts or omissions. In data licenses, a covenant might require the licensor to update the dataset if a source is later identified as restricted, or to maintain compliance processes during the license term. Warranties, by contrast, typically speak to the status of the dataset at the time of delivery or at signing.
• Indemnities shift the financial risk of third‑party claims. If a third party alleges copyright infringement due to the dataset, an indemnity can require the licensor to defend and pay damages, subject to agreed limits. A warranty may be the factual foundation that triggers the indemnity, but they serve different functions: warranty allocates truth of facts; indemnity allocates cost of claims.

There are also jurisdictional nuances:

• US vs. UK usage: In US contracts, “representations and warranties” commonly appear together, and damages analysis can rely on UCC or common law principles. In England and Wales, “warranties” and “representations” often carry distinct remedies; misrepresentation may open rescission and tort damages, so sellers frequently avoid using “representation” language and rely on “warranty” with explicit non‑reliance clauses. For ML data, this affects drafting choices: a UK‑oriented contract may include detailed warranties, disclaim reliance on pre‑contract statements, and frame remedies within negotiated liability caps.

Knowing these distinctions allows you to place the provenance warranty correctly in the contract architecture: it states what is true about the dataset’s origins and acquisition; covenants promise ongoing conduct; and indemnities and limitations of liability control the financial outcomes if something goes wrong.

Step 2: Build the clause—model components for provenance warranties and no‑scraping language, with US/UK variants and common pitfalls

A robust provenance warranty is built from clear, verifiable components. Aim for specificity so that each component can be tested and enforced, and ensure consistency with definitions elsewhere in the agreement (for example, what “Dataset,” “Content,” “Restricted Sources,” and “Permitted Uses” mean).

Core components of a provenance warranty:

• Source authenticity: The licensor warrants that the dataset consists of content from identified or identifiable sources. This may include categorization (publisher content, user‑generated content, public domain materials) and, where possible, traceability metadata. The goal is not to list every URL but to assert that sources are legitimate and not falsified.
• Acquisition method: The licensor warrants that the data was obtained by lawful and authorized means. This addresses compliance with website terms, platform APIs, contracts with data suppliers, and any technical access controls. It should cover avoidance of trespass to chattels, anti‑hacking statutes, and terms‑based prohibitions.
• Rights chain: The licensor warrants it holds sufficient rights to license the dataset for the stated purposes. This includes copyright, database rights (relevant in the EU/UK), contract rights, and moral rights waivers if applicable. It should distinguish between training (ingesting data into models) and output uses, because permission needs may differ.
• Consent and regulatory compliance: The licensor warrants compliance with privacy, consumer protection, and sector‑specific laws. This includes valid consent or another lawful basis for personal data, de‑identification claims, children’s data restrictions, and export controls. The warranty should not promise perfect compliance for every jurisdiction unless the licensor’s processes actually support that promise.
• No‑scraping from restricted sources: The licensor warrants that the dataset does not include content obtained by scraping in violation of law or contract or from sources that explicitly prohibit such collection. “Restricted sources” should be defined, for example, services with paywalls, robots.txt blocks, explicit TDM opt‑outs, or contractual bans.

No‑scraping language requires care to be precise yet compatible with lawful text‑and‑data mining (TDM) and fair use/fair dealing.

• US variant: Consider the interplay with fair use and the Computer Fraud and Abuse Act (CFAA). A well‑drafted clause can prohibit acquisition that violates law, circumvents technical access controls, or breaches affirmative access restrictions, while acknowledging that nothing in the clause prohibits activities that are independently lawful under fair use or other legal defenses for machine learning. Avoid language that could be read to waive statutory rights, but also avoid open‑ended carve‑outs that swallow the prohibition.
• UK/EU variant: Reflect the sui generis database right and the TDM exceptions (notably for non‑commercial research and, in the EU, opt‑out mechanisms for commercial TDM). The clause should recognize that if a rights holder has opted out of the commercial TDM exception, acquisition must respect that opt‑out. For UK law, ensure compatibility with any licensed access and fair dealing limitations. Define “TDM exception” and “Opt‑Out” so the scope is clear.

Common drafting pitfalls to avoid:

• Vague terms: Words like “generally compliant” or “to the best of knowledge” in the main warranty reduce enforceability and invite disputes. If you need knowledge qualifiers, apply them deliberately and define “Knowledge.”
• Over‑broad bans: A blanket “no scraping” clause that forbids any automated collection from public web pages may conflict with fair use or TDM exceptions and may be operationally unworkable. Tailor the clause to prohibited conduct: breach of contract, circumvention of access controls, or ignoring explicit opt‑outs.
• Ambiguous rights to train vs. deploy: If the licensee intends to use model outputs commercially, the warranty should address whether source licenses extend to training and output exploitation; otherwise, the parties should handle this under separate license grants and indemnities.
• Undefined “Restricted Sources”: Without a definition, parties may disagree later. Use illustrative categories and objective signals (e.g., paywalls, API‑only access requirements, robots.txt disallow rules, explicit opt‑out headers or registries).

The aim in this step is to assemble a clause that states concrete, testable facts: what sources are permitted, how access occurred, what rights exist, and what is excluded—including a clear, jurisdiction‑sensitive no‑scraping statement that coexists with lawful exceptions.

Step 3: Allocate and control risk—indemnities, limitations, knowledge/time qualifiers, and carve‑outs (fair use/TDM)

After defining the warranty, align it with the contract’s risk allocation framework so each party knows the financial and procedural consequences if the warranty is untrue or if a third party brings a claim.

• Indemnities (third‑party claims): Couple the provenance warranty with a licensor indemnity for third‑party claims alleging that the dataset or its acquisition infringed IP, violated database rights, breached contract (e.g., terms of service), or violated privacy law. Set clear conditions: prompt notice by the licensee, control of defense by the licensor, cooperation duties, and mitigation obligations. Think about territorial scope; ensure the indemnity mirrors where the dataset will be used and where claims are likely.
• Caps and baskets: Place the indemnity within the limitation of liability structure. A cap limits total financial exposure (for example, a multiple of fees). A basket or deductible prevents small claims from triggering recovery. For data provenance, parties sometimes agree a higher cap for IP/privacy claims than for general breaches, reflecting higher risk severity.
• Exclusions from caps: Consider whether to exclude certain liabilities from caps (e.g., willful misconduct, data protection fines to the extent transferable by law, or breach of confidentiality). Balance is important: licensors resist unlimited exposure; licensees seek meaningful protection for high‑impact claims.
• Knowledge qualifiers: Apply knowledge to certain sensitive assertions (for example, “to licensor’s knowledge, no third‑party terms were breached by contributors”). Define “Knowledge” as the actual knowledge of named roles, after reasonable inquiry according to documented compliance procedures. This avoids turning the warranty into a guarantee of omniscience while preserving accountability for failures in reasonable compliance processes.
• Time limits: Limit the period during which warranty claims can be brought (e.g., 12–24 months after delivery), but align this with the realistic discovery window for data provenance issues. For privacy or IP claims, consider longer periods or separate survival periods.
• Procedures for third‑party claims: Detail how parties defend, settle, and remediate. For example, give the licensor options to procure rights, replace data, or remove problematic subsets. Ensure any removal does not break the licensee’s model integrity without mitigation. The procedure should also address previously trained models: whether retraining or dataset substitution is required and who pays.
• Fair use/TDM carve‑outs: Ensure the indemnity and warranty framework does not accidentally negate lawful defenses. In the US, acknowledging that nothing restricts statutory fair use helps avoid arguments that the contract waives or penalizes lawful activities. In the UK/EU context, reference to TDM exceptions and opt‑outs clarifies that the parties are not promising legality beyond what the law allows or disallows, but are allocating responsibility for compliance.

This step converts the factual promises into a predictable risk‑sharing mechanism. A carefully calibrated set of caps, baskets, knowledge qualifiers, time limits, and procedures ensures that a breach does not automatically become an existential threat to the project, while still motivating careful sourcing and documentation.

Step 4: Evaluate and iterate—checklist and quick redline exercises to improve clarity and enforceability

Evaluation is about testing the clause against common failure points and jurisdictional sensitivities. Use a structured checklist and be ready to iterate the language to reduce ambiguity and bias.

Key evaluation points:

• Enforceability: Are the warranties precise and tied to defined terms? Are remedies and procedures clear? In UK‑oriented agreements, is the distinction between warranty and representation deliberate, with any non‑reliance language consistent with consumer and unfair contract terms law? In US‑oriented agreements, do damages and disclaimers fit within applicable UCC/common law and public policy limits?
• Ambiguity: Do terms like “Restricted Sources,” “Scraping,” “Automated Access,” “Opt‑Out,” and “Publicly Available” have clear definitions? Avoid circular definitions. Ensure that “publicly available” does not inadvertently include paywalled or gated content.
• Jurisdictional sensitivities:
  • DMCA (US): Does the clause avoid suggesting circumvention of technological measures? Ensure no language could be read as authorization to bypass DRM or access controls.
  • CFAA/anti‑hacking laws (US): The no‑scraping clause should align with prohibitions against unauthorized access, especially where access is gated, credentialed, or expressly restricted.
  • Database rights (EU/UK): Confirm that extraction and reutilization are addressed and that rights have been licensed or the TDM exception applies. Verify handling of opt‑outs under EU commercial TDM rules.
  • TDM exceptions (UK/EU): Confirm compatibility with permitted exceptions and any opt‑out signals or registries. For UK contracts with EU data, address cross‑border implications.
• Bias toward a party: Check whether the clause allocates disproportionate risk. For example, a licensor‑friendly draft might include broad disclaimers and narrow warranties; a licensee‑friendly draft might impose uncapped indemnity and absolute warranties. Aim for calibrated symmetry: clear warranties matched with reasonable caps and remediation rights.
• Operational fit: Can the licensor actually perform the promised due diligence (source tracking, consent records, term monitoring)? Can the licensee implement takedown, replacement, or retraining workflows? Contract terms that exceed operational capacity create hidden risk.
• Consistency with privacy and security terms: If the dataset contains personal data, ensure privacy exhibits and data processing agreements align with the provenance warranties. Confirm that de‑identification claims are accurate and testable under relevant standards.

Iteration techniques:

• Tighten definitions first: Clarity in defined terms resolves many downstream disputes. If a definition is unclear, start there rather than patching the operative language.
• Separate present‑fact warranties from ongoing covenants: If the licensor must monitor opt‑out registries or update compliance as laws change, state these as covenants with realistic response times and update mechanisms.
• Use scoped knowledge qualifiers: Where uncertainty is unavoidable, limit knowledge qualifiers to named roles and documented processes. Avoid blanket knowledge qualifiers on every warranty; they dilute accountability.
• Align remedies with technical realities: If removing data subsets post‑training is technically impractical, the contract should say whether removal is limited to future training datasets, whether model weights must be retrained, or whether alternative mitigation (e.g., feature suppression) is acceptable.
• Cross‑reference with license grant and use scope: The warranty depends on the permitted uses. If outputs are restricted, make sure the rights chain supports those restrictions; if outputs are allowed, ensure that the warranty is not narrower than the use grant.

By applying this checklist and iterating language, you move from a theoretically correct clause to one that is enforceable, balanced, and workable. The drafting discipline—define facts, assemble components with jurisdictional awareness, allocate risk with precision, and test against real operational constraints—produces provenance warranties and no‑scraping language that fit the modern ML data environment. The result is a contract that reduces legal uncertainty while allowing lawful, responsible development of machine learning models, even across complex, multi‑source datasets and evolving regulatory landscapes.