Data used to fit a model; spans the AI Act's legal definition, ML practice, and provenance/copyright disputes.
On the ward, training data is the answer to the question 'which patients did this tool learn from?' Clinicians operationalize it as the training cohort's demographics, care settings, devices and era, read against the patient in front of them. A decision-support output is treated as transferable only when the local population plausibly resembles that cohort; when it does not — a different age group, comorbidity mix or scanner — the output is downgraded from evidence to a prompt for clinical judgment, and the mismatch is worth reporting rather than silently working around.
Locate the training-population description in a tool's labeling or model card, compare it with your local patient population, and escalate suspected mismatch instead of silently over- or under-trusting outputs.
The Epic sepsis model showed much weaker discrimination in external validation than in development (Wong et al. 2021, JAMA Internal Medicine) — a training-cohort transferability failure clinicians experienced as alert fatigue.
OmniGloss seed synthesis, 2026 (machine-drafted, pending expert validation)
Mitchell et al., 'Model Cards for Model Reporting', FAT* 2019
S. Finlayson et al. (2021). The Clinician and Dataset Shift in Artificial Intelligence. The New England Journal of Medicine
For hospital committees authorizing clinical AI, training data is a due-diligence object: before deployment is approved, the vendor must evidence what data the system was trained on, under which lawful basis and ethics approvals, and whether the training population is representative of the intended patient population — the relevance-to-the-clinical-problem expectation that FDA's proposed good machine learning practices articulate for AI/ML-based medical software. Absent or vague training-data evidence is treated as a procurement red flag and a patient-safety risk in its own right, whatever headline accuracy the vendor reports, because unrepresentative training data is the mechanism by which validated-looking tools fail specific patient groups.
Require vendors to disclose training-cohort composition, provenance and lawful basis, and weigh representativeness against your intended-use population before authorizing clinical deployment.
e.g., a procurement board rejects a dermatology triage app whose training images under-represent darker skin types, judging the reported overall accuracy uninformative for its own patient mix.
U.S. FDA, Proposed Regulatory Framework for Modifications to AI/ML-Based Software as a Medical Device (discussion paper, 2019)
good machine learning practices; data relevance to the clinical problem
In clinical ML engineering, training data is the fitted partition: the labeled patient records actually used to estimate model parameters, bounded by explicit inclusion and exclusion criteria and an extraction date, and separated from validation and test data by patient-level splits so no individual contributes to both fitting and evaluation. It is versioned with the code, characterized alongside the model (sites, era, devices, label source) and frozen before evaluation begins, because any later contact between training and evaluation records silently invalidates every reported performance number.
Define cohort criteria, split at patient level before any tuning, version and freeze the training partition, and document its composition alongside the model.
e.g., a team building a readmission model finds the same patients' admissions in both training and test splits, inflating AUC; patient-level re-splitting drops performance to its honest level.
NIST AI 100-3, The Language of Trustworthy AI: An In-Depth Glossary of Terms
training data (citing ISO/IEC 22989: samples for training used to fit a machine learning model)
Among clinical ML practitioners who work close to the chart, training data is only as meaningful as its labels, and those labels are clinical judgments, not facts of nature. This school operationalizes training data as record plus label provenance: who annotated, under what protocol, with what inter-rater agreement, and whether labels came from adjudicated expert reads, billing codes, or NLP over free-text reports. A large dataset without label provenance is treated as unusable for safety-relevant training; a smaller adjudicated one is preferred, because label noise becomes model behavior at the bedside.
Record the source, protocol and inter-rater agreement of every training label, and reject or re-annotate corpora whose label provenance cannot be established.
CheXpert (Irvin et al. 2019) derives chest X-ray training labels from radiology reports via an automated labeler, importing report ambiguity into the labels — exactly the provenance this school insists on surfacing.
Live variance among clinical ML teams: scale-first practice accepts noisy report- or claims-derived labels as adequate training data, while adjudication-first practice treats label provenance as constitutive of the training data itself rather than a separate ground-truth concern.
Gebru et al., 'Datasheets for Datasets', Communications of the ACM 64(12), 2021
questions on labeling and collection process
In health-data governance and audit practice, training data is a legally bounded category: precisely the patient records used to fit the model's learnable parameters, kept distinct from validation and testing data because distinct duties attach — a lawful basis for secondary use of health data under GDPR, data-governance and representativeness requirements for training datasets under the AI Act, and documentation from which an auditor can reconstruct exactly which records trained the system. Data outside that boundary is still governed, but under other provisions; the sharpness of the boundary is what makes an audit scopable and a certification defensible.
Verify a lawful basis for each training-data source, reconstruct which records fitted the model, and check documentation against AI Act data-governance requirements before certifying.
e.g., an audit of a hospital's deterioration model finds validation extracts commingled with the fitted cohort in one undocumented data lake, making it impossible to say which patient records require a GDPR Article 9 basis for training use.
One position in the recorded scope dissensus: production-engineering communities bound 'training data' functionally as the whole behavior-shaping data estate, which this practice resists as unauditable; here the statutory fitted-partition boundary is the condition of auditability.
For underwriters and credit analysts working with scorecards, training data means the development sample: the book of historical applications and outcomes, from a stated origination window with stated exclusions, on which the score was built. The score is read as a summary of that book, nothing more. Practical literacy is knowing the window and segment coverage — and treating the score as weaker evidence whenever today's applicant, product or economic conditions sit outside them, applying documented overlays or referring the case rather than lending the number a precision it does not have.
Identify the development window and segment coverage behind a score, and apply overlays or refer cases falling outside them rather than treating the score as universally valid.
e.g., during 2020-2021 payment moratoria, repayment histories stopped reflecting borrower risk, and analysts were instructed to overlay judgment on scorecards developed on pre-pandemic training data.
Board of Governors of the Federal Reserve System, SR Letter 11-7: Supervisory Guidance on Model Risk Management (2011)
model development, implementation, and use — data relevance and limitations
For model owners and risk committees, signing off a model means accepting its training data as part of the model risk being taken. The development sample's window, sources, exclusions and known gaps are reviewed as documented properties, and their suitability for the intended portfolio and current conditions is judged before approval, consistent with supervisory guidance that development data be assessed for suitability and documented. Deficient or undocumented training data does not merely lower confidence; it becomes a condition of approval — a use restriction, a compensating overlay, or a remediation deadline the owner must track.
Review the development sample's window, sources and gaps before authorizing model use, and convert identified training-data weaknesses into explicit use restrictions, overlays or remediation actions.
e.g., a risk committee approves a small-business scorecard built on 2015-2019 originations for existing products only, requiring redevelopment before any use on a newly launched lending segment.
model development; governance, policies, and controls
For credit-model developers, training data is a constructed development sample, not a found dataset: an origination window paired with a performance window, an explicit outcome definition such as ninety days past due, documented exclusions, and treatment of the selection problem that only approved applicants have observed outcomes — typically via reject inference. Segment coverage is checked so thin populations are visible, and an out-of-time sample is carved off before fitting begins. The sample's construction script is part of the model, expected to be re-runnable end to end at validation.
Construct the development sample with explicit outcome definitions, exclusions and reject-inference treatment, reserve an out-of-time sample, and keep sample construction re-runnable end to end.
e.g., an application scorecard fitted only on approved loans misestimates risk for marginal applicants; the modeler augments training data through reject inference and validates on an out-of-time sample.
model development, implementation, and use — data quality and relevance
For teams running model pipelines in production, 'training data' names a moving estate, not a frozen table: everything the pipeline feeds into fitting and refitting — the base development sample, the validation partitions used for hyperparameter selection, monitoring outcomes recycled into scheduled retraining, and vendor data joined at build time. Because all of it shapes the deployed model's behavior, all of it is placed under the same lineage, versioning and access controls; drawing the governance line around the statute's fitted partition alone is regarded, in this practice, as an audit fiction.
Register every data flow that shapes model behavior — fitting sets, tuning partitions, retraining feeds — under common lineage, versioning and access controls.
e.g., a fraud model retrained weekly on labeled chargebacks: last month's production outcomes are this month's training data, so the team registers the feedback loop itself as a governed data asset.
One position in the recorded scope dissensus: regulatory and audit practice bounds training data at the statutorily fitted partition, which this operational community regards as too narrow to govern a continuously retrained system.
In independent validation practice shaped by supervisory guidance, training data is evidence to be re-derived, not description to be accepted. Validators trace the development sample back to source systems, re-execute filters and exclusions, test the data's relevance to current products, markets and conditions, and probe how missing values, proxies and manual adjustments were handled. Undocumented sample construction is itself a finding, independent of measured performance, because a model whose training data cannot be reproduced cannot be subjected to effective challenge — and effective challenge is what the validation function exists to provide.
Reconcile the training sample to source systems, re-execute its construction, assess its relevance to current conditions, and raise findings where construction cannot be reproduced or justified.
e.g., validation of a probability-of-default model finds restructured loans were excluded from the development sample without documentation; the model is approved only for segments where the exclusion is immaterial.
model validation — evaluation of conceptual soundness; effective challenge
In fair-lending and conduct review, training data is read as a record of past lending practice — including the discriminatory geography of credit access — that a model will faithfully reproduce as prediction unless interrupted. The operational questions are whose outcomes the data contain and whose they structurally lack: populations historically denied credit appear rarely or with truncated outcomes, so a model can learn the deprivation itself as risk. Reviewers therefore test training data for prohibited-basis proxies and disparate-impact potential before any accuracy argument is allowed to settle the matter.
Test training data for prohibited-basis proxies, absent or truncated populations, and disparate-impact potential, and require mitigation before accepting accuracy as sufficient justification.
e.g., mortgage data reflecting decades of redlining encodes depressed valuations in formerly redlined neighborhoods; a valuation model trained on it perpetuates the discount however well its backtests perform.
Contested within assurance functions: model-validation practice operationalizes training data as a technical-suitability question, while fair-lending review operationalizes the same data as an inheritance of historical discrimination requiring disparate-impact testing; the two yield different acceptance criteria for the same model.
Schwartz et al., NIST Special Publication 1270: Towards a Standard for Identifying and Managing Bias in Artificial Intelligence (2022)
systemic and historical bias in datasets
Barocas, Hardt and Narayanan, Fairness and Machine Learning: Limitations and Opportunities (MIT Press, 2023)
training data as a record of past decisions
Among working artists, illustrators, writers and musicians, training data is the body of creative labor a generative system was built from — overwhelmingly other people's works, gathered without invitation. The operational questions are ownership questions: is my work in the corpus, was I asked, am I credited, am I paid, and can I get out? Corpus membership is probed practically, through style-mimicry prompts and dataset search tools such as Have I Been Trained, and a tool's legitimacy is judged by the consent status of its corpus, not by the quality of its outputs.
Check whether your own or your sources' works appear in a tool's training corpus, exercise available opt-outs, and factor corpus consent status into whether you use or endorse the tool.
Illustrators in Andersen v. Stability AI (filed 2023) allege their works were used as image-generator training data without consent — the paradigmatic grievance of this operationalization.
One position in the recorded training-data copyright dissensus: opposes the developer position that lawful accessibility plus honored opt-outs legitimates use of works as training data.
Harry H. Jiang et al. (2023). AI Art and its Impact on Artists. Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society
Lin Kyi et al. (2025). Governance of Generative AI in Creative Work: Consent, Credit, Compensation, and Beyond. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
For publishers, studios and agencies deciding which generative tools to adopt, training data is a rights and liability question before it is a technical one: an input the provider must be able to warrant. Adoption turns on whether the provider can evidence licensed, owned or demonstrably lawful training sources, offers indemnification against infringement claims arising from training, and respects the organization's own reserved rights in its catalogues — because outputs of a tool trained on unlicensed works can contaminate the product with claims the organization must then defend at its own cost.
Require contractual warranties and indemnities on training-data provenance before adopting a generative tool, and verify the provider honors your organization's own rights reservations.
Adobe marketed Firefly as trained on Adobe Stock and licensed or public-domain content, backed by commercial indemnification — a purchasing criterion built directly on training-data provenance.
One position in the recorded training-data copyright dissensus: aligns with the consent-and-licensing position, operationalized at procurement as warranty and indemnification requirements rather than moral claims.
For teams building generative models, training data is a web-scale corpus engineering problem: billions of text-image pairs or documents assembled from crawls and archives, then filtered by deduplication, quality and safety classifiers, with machine-readable rights reservations honored at crawl or filter time. The corpus is characterized statistically — language mix, domain mix, licence mix — rather than item by item; individual works matter as distribution mass, not as titles. Lawful accessibility plus opt-out compliance, not per-work licensing, is this community's working criterion of a usable corpus.
Assemble corpora from lawfully accessible sources, implement deduplication, quality filtering and machine-readable opt-out compliance, and characterize corpus composition statistically for downstream documentation.
LAION-5B (Schuhmann et al. 2022), 5.85 billion crawl-derived image-text pairs distributed as URLs plus metadata, typifies the corpus this practice assembles and filters.
One position in the recorded training-data copyright dissensus: creator and rightsholder communities reject lawful access plus opt-out as sufficient, demanding per-work consent, credit and compensation.
Inside media and AI production houses, a distinct engineering practice treats a training corpus as undocumented until proven otherwise: no corpus enters a build unless it ships with a datasheet stating motivation, composition, collection and labeling process, licence status, and recommended or discouraged uses. Training data here is operationalized as artifact plus paperwork — the datasheet is the interface through which downstream teams, counsel and clients can answer what a model was trained on without forensic archaeology, and its absence blocks the pipeline the way a missing licence blocks a release.
Produce or demand a datasheet for every training corpus before use, and block builds on corpora whose composition, collection process or licence status is undocumented.
e.g., a games studio requires an internal datasheet for any corpus used to fine-tune dialogue models, so legal can trace each source's licence when a claim arrives.
datasheet questions: motivation, composition, collection, uses
Karen L. Boyd (2021). Datasheets for Datasets help ML Engineers Notice and Understand Ethical Issues in Training Data. Proceedings of the ACM on Human-Computer Interaction
For rights-clearance and AI-compliance stewards, training data is a disclosure object with legal teeth: providers of general-purpose AI models must publish a sufficiently detailed summary of the content used for training and maintain a policy to identify and honor reservations of rights under Union copyright law. The steward operationalizes training data as whatever those instruments must reveal — at a granularity that lets a rightholder determine whether their catalogue was used — and audits provider documentation against that bar rather than against engineering convenience.
Assess providers' training-content summaries and copyright policies for sufficient granularity, compare disclosed sources against represented catalogues, and escalate inadequate disclosure toward enforcement or licensing action.
e.g., a collecting society compares a provider's published training-content summary against its members' catalogues to decide whether to open licensing negotiations or file a complaint.
In case handling, training data is the archive of past cases a decision-support tool learned from, and a score is a summary of how files like this one were decided before. Caseworkers operationalize the concept by asking whether the current file is the kind of case the archive contains: same scheme, comparable circumstances, rules unchanged since the archive closed. When the file diverges — a new benefit type, a rare constellation, a legal amendment postdating the data — the score is set aside, the case is worked manually, and the divergence is recorded so the mismatch becomes visible upstream.
Treat scores as summaries of past similar cases, check whether the current case falls within what the system learned from, and record and escalate divergences instead of deferring.
e.g., a caseworker handling the first applications under a newly introduced benefit sets the triage score aside, since no such cases existed in the training archive.
For officials who authorize systems, training data is inherited administrative practice: years of recorded decisions by a particular bureaucracy, with its enforcement priorities, blind spots and errors, converted into tomorrow's defaults at scale. Authorization is therefore an act of endorsement — a decision that the practice captured in those years deserves continuation. The operational questions are which years, which offices and which decisions the data span, and whether any documented episode inside that span — litigated, apologized for, or since reformed — is about to be quietly automated forward.
Before authorizing, establish which decisions and periods the training data encode, and refuse or condition deployment where the encoded practice includes episodes since found unlawful or unjust.
In the Dutch childcare-benefits scandal, risk scoring trained on past enforcement data and using nationality-linked features helped wrongly brand thousands of families as fraudsters, contributing to the government's resignation in 2021.
For government data teams, training data is rarely collected fresh; it is assembled from what the state already holds — benefit registers, case-management extracts, inter-agency linkages made under legal gateways. The working operationalization is the documented assembly: which registers, under which legal basis and sharing agreement, linked on which keys, cut at which reference date. That record exists because the assembly must survive three futures: a freedom-of-information request, an algorithm-register entry, and a schema change in a source register that forces the whole training set to be rebuilt.
Document every training-data assembly — source registers, legal gateways, linkage keys, reference dates — so it can be disclosed on request and rebuilt when sources change.
Amsterdam's and Helsinki's public algorithm registers, launched in 2020, list data sources per deployed system — the disclosure target this documentation practice anticipates.
In official-statistics production, a training set is a methodological input subject to the same quality regime as any other statistical source. When machine learning assists coding, editing or imputation, statisticians operationalize training data through the quality lens of the European statistics framework: coverage of the target population, timeliness relative to the reference period, coherence with statistical concepts and classifications, and documented construction in the methodology report. A classifier's training set is treated like a survey frame — its deficiencies propagate into official figures and must be quantified, published and defensible.
Assess a training set's coverage, timeliness and coherence like any statistical source, and document its construction in the methodology report so results remain reproducible and contestable.
e.g., a statistics office training a classifier to assign free-text job descriptions to ISCO-08 codes builds its training set from manually double-coded records and reports coding accuracy by occupation group.
European Statistics Code of Practice, revised edition 2017 (Eurostat/ESGAB)
Principles 7, 8 and 12 (sound methodology, appropriate procedures, accuracy and reliability)
For public-sector audit and oversight bodies, training data is a matter of answerability: when an algorithmic system informs decisions about citizens, the administration must be able to state, on the record and to a court, what data trained it. The operationalization is reconstructability — procurement files, impact assessments and system documentation must identify training sources and their legal bases well enough for a judge, ombudsman or FOI requester to test whether the system's learned patterns are compatible with lawfulness, equality and reasoned decision-making. A system whose training data cannot be reconstructed is unauditable, and its decisions are legally vulnerable.
Test whether training sources, legal bases and assembly records can be reconstructed from official documentation, and report systems that fail reconstruction as accountability defects.
In the SyRI judgment (The Hague District Court, 2020), Dutch welfare-fraud risk scoring was halted partly because the state would not disclose what data and indicators shaped risk models, failing Article 8 ECHR scrutiny.
In operational crop monitoring, training data is an administrative by-product before it is a curated asset: farmers' subsidy declarations and land-parcel identification (LPIS) boundaries are recycled as labels for classifier training, alongside LUCAS points and field campaigns. The infrastructural questions are versioning per campaign year — varieties, practices, and parcel geometries change annually — and label circularity: declarations contain the very errors the classifier is supposed to detect, so training pipelines must document label origin and independence from the control task.
Document the origin, campaign year, and error properties of every label source, and ensure training labels are independent of the declarations the model will be used to check.
e.g., a paying agency trains its crop classifier on the season's own declarations; misdeclared parcels enter as correct labels, and the model learns to confirm the claims it exists to verify.
In defense AI programs, training data is a governed commodity moving through classified infrastructure: operational sensor data is collected under specific authorities, marked with classification and releasability, and often cannot be pooled across systems, coalition partners, or contractors without downgrading or agreement. Labeling requires cleared annotators; scarce real examples of threat systems are augmented with synthetic and surrogate data; and datasets are curated, versioned, and access-controlled as accreditation artifacts, because a poisoned or mislabeled corpus is an attack surface. Pipeline design is therefore as much security architecture as data engineering.
Manage training corpora as classified, versioned accreditation artifacts: verify collection authority and releasability, control annotator access, and screen ingestion pipelines for poisoning and mislabeling.
E.g., a coalition object-recognition effort stalls because each nation's drone footage is classified nationally; the model is trained on releasable and synthetic imagery, and the accreditation file records the substitution.
For educational institutions and edtech vendors, training data is student work and student behavior put to a second use: essays feeding scoring engines, clickstreams tuning adaptive platforms, recorded exams training proctoring classifiers. The operational questions are legal and fiduciary before they are technical — under what lawful basis and whose consent may a minor's coursework train a commercial model, does the vendor contract permit it, and does the derived model memorize identifiable learner material? Institutions operationalize this through contract clauses restricting secondary use, transparency notices to students and parents, and refusal to license pupil data for vendor model development without explicit agreement.
Interrogate every edtech contract for secondary-use clauses, establish a lawful basis before learner work trains any model, and disclose such uses to students and parents.
Turnitin's practice of archiving submitted student essays to power its similarity-detection database was challenged in court; in A.V. v. iParadigms (2009) a US appeals court held the archiving to be fair use.
Who may put learner work to model-training use is genuinely disputed: edtech vendors read broad terms-of-service licenses as covering product improvement and model training, while institutions, students, and data-protection officers increasingly treat coursework as material requiring an explicit lawful basis and negotiated contract terms before any secondary use.
For manufacturing AI teams, training data is a curated defect and process library with the logistics problem built in: good parts are abundant, the failures that matter are rare, and some defect classes exist only as a handful of images from one bad week. Building the asset means capturing images and telemetry under documented conditions — camera, lighting, line, material lot — labeling against boundary samples, sometimes inducing defects on scrap parts to fill empty classes, and versioning the library like any controlled document, because the model must be retrained and requalified when the process or product revision changes.
Document capture conditions and label provenance for every training set, manage class imbalance deliberately, and version the library so each deployed model traces to an exact data state.
e.g., a team machines seeded flaws into scrap castings to give a porosity class thirty real examples, logging each induced defect so synthetic and natural cases stay distinguishable.
Gebru et al. 2021, Datasheets for Datasets
documenting dataset collection conditions and composition
In legal analysis, training data is a bundle of rights and exposures before it is a corpus: each item raises copyright and database-right questions, contractual and licence restrictions, confidentiality obligations, and data-protection bases, and the AI Act's definition — data used to fit a model's learnable parameters — marks where those questions attach. Counsel operationalize the term through diligence and discovery: what was ingested, under what licence or exception, whether client or personal data entered a vendor's training pipeline, and what warranties and indemnities allocate the resulting infringement and confidentiality risk in the contract.
In AI vendor diligence, obtain a warranted account of training-data sources and licences, verify contractual limits on the vendor training on client inputs, and allocate infringement risk through indemnities.
The New York Times' 2023 copyright suit against OpenAI and Microsoft turns on whether ingesting its articles as training data was infringing reproduction — a live test of how courts will characterize training corpora.
Whether ingesting protected works for model training is infringing reproduction or lawful under fair use or text-and-data-mining exceptions is being actively litigated, dividing rights-holder counsel from counsel for model developers, with answers differing across jurisdictions.
In carrier data platforms, training data is the historical operational exhaust assembled to fit predictive components: telematics traces, scan and milestone events, realized arrival times, dwell and dock times, weather and traffic archives, and customs-clearance durations, joined per shipment and lane. Its assembly is an infrastructure problem, deduplicating events across carrier systems, aligning clocks and time zones, and labeling realized arrivals as targets, and its coverage determines which lanes the model can serve; a corridor absent from the archive is a corridor the ETA model will guess at.
Curate lane-representative historical event data with verified arrival labels, document coverage gaps by corridor and season, and gate retraining on the freshness and completeness of the archive.
e.g., an ETA model retrained after a network acquisition whose archive lacks the acquired depots' scan history, leaving three months of unreliable predictions on the new corridors.
Art. 3(29): data used for training an AI system through fitting its learnable parameters
In the platform economy, training data is yesterday's judgments given tomorrow's force: the archive of star ratings, tips, complaints, cancellations, and acceptance logs on which ranking, dispatch, and deactivation models are fitted. Because that archive records customers' prejudices, past managerial choices, and the unequal conditions under which people worked — night shifts, poor neighbourhoods, old phones — models trained on it re-issue those conditions as scores. Workers rarely know they are generating it; every tap and swipe on shift is unpaid annotation for the system that will supervise them.
Identify which recorded worker and guest behaviours feed model training, and assess what historical prejudice and unequal working conditions those records carry into future allocation.
e.g., a cleaning platform trains its reliability score on historical cancellations, encoding the childcare constraints of single parents as low reliability and pushing them down the offer queue.
Platforms treat operational logs as legitimate training material they own under the terms of service; worker advocates and some data-protection authorities contest both the legal basis and the fairness of turning a worker's history into the instrument that disciplines them.
In retail data operations, training data is the slice of the customer estate — loyalty transactions, browsing events, email engagement, CRM attributes — piped into fitting propensity, recommendation, and lookalike models, together with the consent state and purpose basis attached to each record. Operationally it is a pipeline artifact: extraction filtered by consent flags, identity-resolved, labeled with campaign outcomes, and versioned so a model's audience can be reconstructed. The unresolved plumbing question is which consented purposes travel with the data when it is reused for model fitting rather than for the campaign it was collected for.
Filter training extracts by recorded consent purpose, version each training set so model audiences are reconstructable, and obtain a documented legal basis before reusing campaign data for model fitting.
e.g., a supermarket's lookalike model is trained on loyalty-card purchase histories collected to personalize offers; when regulators ask whether that purpose covers model training, no documented assessment exists.
Growth and data-science teams treat consented marketing data as reusable for any model serving marketing; privacy counsel read purpose limitation as requiring a separate basis for training — the sector lacks a settled line.
Art. 3(29) (training data as data used to fit learnable parameters)
For research groups building or adapting models, training data is a versioned, citable research object rather than an input folder: a fixed snapshot with a persistent identifier, a documented collection and inclusion procedure, licence and consent conditions recorded per source, and declared splits frozen before any evaluation is run. Groups maintain the deposit, the datasheet, and the exclusion log together, because reviewers and re-users must be able to establish what the model actually saw. Text-and-data-mining rights, the scope of participant consent, and contamination of downstream benchmarks are all adjudicated from that record.
Version and document every training corpus with its sources, licences, and inclusion rules, freeze the splits before evaluation, and be able to show what the model was and was not exposed to.
e.g., a fine-tuned model whose reported benchmark gain evaporates once the archived training snapshot is checked against the test items and overlap is found, a contamination check possible only because the snapshot was frozen and deposited.
Gebru et al., 'Datasheets for Datasets', Communications of the ACM (2021)
composition, collection-process and preprocessing documentation questions
Jack Bandy et al. (2021). Addressing "Documentation Debt" in Machine Learning Research: A Retrospective Datasheet for BookCorpus. ArXiv
For ML platform teams, training data is a versioned supply chain: corpora and feature sets with recorded provenance, licenses, snapshots, and splits, connected by lineage metadata to every model fitted on them. It is operationalized through dataset registries, datasheet-style documentation, deduplication and contamination checks against evaluation sets, and reproducible ingestion pipelines - because a model is only as auditable, licensable, and re-trainable as the data path behind it. In AI Act terms this is the data used to fit learnable parameters, and providers must be able to account for it.
Version and document every training corpus with provenance and license metadata, check for evaluation-set contamination, and keep lineage from dataset snapshot to deployed model reproducible.
The Books3 corpus was withdrawn in 2023 after copyright enforcement action, forcing teams that had trained on it to establish what exactly was in their data pipelines - a provenance question many could not answer quickly.
Gebru et al., Datasheets for Datasets (CACM 2021)
datasheet documentation of dataset motivation, composition, and collection