AI Training Data & Licensing Glossary

Understand the terms behind AI training data and business data licensing—from workflows and model evaluation to privacy, provenance, and usage rights. Clear explanations to help you understand how data is used and what to consider before licensing yours.

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60 terms · All categories

A

AI agents: AI agents are systems that use observations and decisions to take actions toward a goal. Their capabilities and autonomy vary; tools, permissions, memory, stopping rules, and outcome checks shape what they can reliably accomplish.; AI training, agents & evaluation
AI training data: AI training data is the information used to adjust a model’s learned behavior or parameters. It can include text, images, actions, labels, or other signals; usefulness depends on the training objective, representation, quality, and permissions.; AI training, agents & evaluation
Anonymization: Anonymization aims to make information no longer attributable to identifiable people under the applicable standard. It is a demanding, context-dependent outcome; masking names or replacing them with tokens does not establish it.; Rights, privacy & control

C

Chain of rights: A chain of rights is the documented sequence of permissions or transfers supporting a party’s authority to use and license material. It connects original creation and collection to the specific rights offered to a recipient.; Rights, privacy & control
Computer-use agent: A computer-use agent is an AI system that pursues a task by interacting with software interfaces, such as clicking controls, entering text, and moving between applications. Its actions need permissions, feedback, and checks against the intended outcome.; AI training, agents & evaluation
Confidential business information: Confidential business information is non-public information subject to duties or expectations of restricted access and use. It can include pricing, customer terms, processes, and commercial plans; some, but not all, may qualify as trade secrets.; Rights, privacy & control
Content provenance (C2PA): C2PA is a technical standard for recording and verifying signed provenance information associated with digital content. It can help inspect a content history, but it does not establish that a claim is true or that all licensing rights are cleared.; Rights, privacy & control
Cross-system context: Cross-system context connects related information from different business applications so a task or outcome can be understood. It depends on reliable identities, timing, and field meanings; simply joining tables does not establish a correct relationship.; Business data & workflows

D

Dark data: Dark data is information an organization collects or retains but does not meaningfully use for analysis or decisions. It may be overlooked, difficult to access, poorly documented, or retained without a clear current purpose.; Business data & workflows
Data annotation: Data annotation adds labels or other structured information to source data so a task can be learned or evaluated. Examples include classifying text, marking objects, identifying actions, or recording whether a workflow reached an intended outcome.; Data quality & preparation
Data curation: Data curation selects, organizes, documents, and maintains data for a defined use. It combines decisions about inclusion and context with quality checks; it is broader than simply cleaning errors or changing file formats.; Data quality & preparation
Data deduplication: Data deduplication identifies and removes or consolidates repeated records or content. Exact and near-duplicate detection require different methods, and repeated business events must be distinguished from accidental copies.; Data quality & preparation
Data exclusivity: Data exclusivity restricts whether a provider can license the same data to other recipients. Its value and practical effect depend on which data, uses, markets, parties, and time periods the restriction covers.; Licensing & economics
Data leakage: In model assessment, data leakage occurs when training or evaluation uses information that would not legitimately be available for the intended prediction. It can inflate performance through overlap, future outcomes, or improper preprocessing.; AI training, agents & evaluation
Data licensing: Data licensing is an agreement that permits specified uses of a dataset under defined conditions. It can address access, AI training, sharing, payment, duration, and restrictions without necessarily transferring the underlying rights.; Licensing & economics
Data lineage: Data lineage traces how data moves and changes between sources, processing steps, and outputs. It helps explain a derived value or dataset version and assess the impact of corrections, exclusions, or upstream changes.; Data quality & preparation
Data minimization: Data minimization limits collection, use, or disclosure to information appropriate and necessary for a defined purpose. It asks which fields and records are needed before relying on later cleanup or access restrictions.; Rights, privacy & control
Data monetization: Data monetization is the creation of economic value from data, including internal improvements, data-enabled products, or licensing. Having a large archive does not by itself establish an external market or predictable revenue.; Licensing & economics
Data ownership: Data ownership is a shorthand for the rights and control a party has over information. Those rights can come from contracts, intellectual property, privacy rules, or other law; storing data does not establish unrestricted licensing authority.; Licensing & economics
Data provenance: Data provenance records where data came from and the people, systems, or activities involved in producing it. It helps assess origin and trustworthiness, while separate rights documentation establishes what uses are authorized.; Data quality & preparation
Data quality: Data quality is the degree to which data is reliable and fit for a particular use. Accuracy, completeness, consistency, timeliness, coverage, and valid interpretation can all matter; quality cannot be judged from file size alone.; Data quality & preparation
Data retention: Data retention defines how long information is kept and what happens when that period or purpose ends. Source files, backups, derived datasets, logs, and model artifacts may need distinct treatment in a licensing agreement.; Rights, privacy & control
Dataset documentation: Dataset documentation explains a dataset’s contents, origins, preparation, intended uses, limitations, and access conditions. It helps recipients interpret records correctly and assess suitability without relying on assumptions or an unexplained export.; Data quality & preparation
Dataset valuation: Dataset valuation is an assessment of a dataset’s economic value in a particular context. Utility, rights, quality, scarcity, preparation costs, and the terms of access can matter more than raw record count.; Licensing & economics
De-identification: De-identification is a process for reducing the association between data and identifiable people or organizations. It combines transformations and disclosure controls; removing names alone does not establish anonymity or eliminate re-identification risk.; Rights, privacy & control

E

Egocentric video: Egocentric video records activity from a participant’s viewpoint, often using a wearable camera. It can show hands, objects, and immediate task context, but camera placement and movement limit what the footage reveals.; Video, robotics & physical AI
Evaluation data: Evaluation data is used to assess a model’s performance on defined tasks or conditions. It should support meaningful comparisons and avoid unintended overlap or information that makes the assessment easier than the real use case.; AI training, agents & evaluation
Expert demonstrations: Expert demonstrations are examples of a skilled operator performing a task. They can show actions, context, and outcomes for imitation or evaluation, but expertise and success need evidence rather than being inferred from the demonstrator’s title.; AI training, agents & evaluation

F

Fair use: Fair use is a U.S. copyright doctrine that can permit certain uses without the copyright holder’s authorization. It requires a context-specific assessment of statutory factors and does not automatically authorize every use of material for AI.; Rights, privacy & control
Fine-tuning: Fine-tuning adapts a pre-trained model through additional training for a particular task, domain, or behavior. It changes learned parameters or additional trainable components and is distinct from simply supplying instructions or retrieving documents at runtime.; AI training, agents & evaluation
Fine-tuning data: Fine-tuning data is the dataset used to adapt an already trained model to a more specific task or behavior. Its format and examples depend on the training method, and it should remain appropriately separated from final evaluation data.; AI training, agents & evaluation

I

Indemnification: Indemnification is a contractual allocation of responsibility for specified losses or claims. In a data license, the covered events, limits, exclusions, defense process, and party obligations determine what protection the clause actually provides.; Rights, privacy & control

L

Licensing scope: Licensing scope is the full boundary of a data license: covered records, permitted activities, authorized parties, duration, geography, and other conditions. Clear scope connects commercial expectations to what the recipient can actually do.; Licensing & economics
Long-horizon task: A long-horizon task requires many connected decisions or actions before reaching its goal. Difficulty comes from dependencies, delayed feedback, and recovery needs—not only from elapsed time or the number of clicks.; AI training, agents & evaluation

M

Model weights: Model weights are learned numerical parameters that influence a model’s predictions or actions. Training adjusts them, while inference uses them; weights are distinct from the source dataset, although models can sometimes memorize aspects of training data.; AI training, agents & evaluation
Multi-camera video: Multi-camera video records a scene or activity from more than one camera. For joint analysis, synchronization, calibration, viewpoint coverage, and consistent event identity determine whether the recordings can be interpreted together.; Video, robotics & physical AI

N

Non-exclusive licensing: Non-exclusive licensing grants permission without reserving the covered rights to one recipient. A provider may retain the ability to grant other licenses, subject to the actual agreement and any pre-existing restrictions.; Licensing & economics

O

Onward sharing: Onward sharing is the transfer or disclosure of data by a recipient to another party. Affiliates, contractors, research partners, and downstream customers can introduce access and use questions beyond the original license.; Rights, privacy & control
Operational data: Operational data is information produced while a business carries out its everyday activities. Orders, service events, inventory changes, and approvals can describe real processes, but their meaning depends on the systems and practices that created them.; Business data & workflows
Outcome labels: Outcome labels describe the result of an event, decision, or workflow under a defined rule. They can support training or evaluation, but labels such as “successful” need evidence, timing, and a clear distinction from intermediate statuses.; Data quality & preparation

P

Permitted use: Permitted use defines the activities a recipient is authorized to perform with licensed data. Analysis, model training, evaluation, redistribution, and use of derived artifacts can require different permissions in the agreement.; Licensing & economics
Personally identifiable information (PII): Personally identifiable information, or PII, is information that can identify a person directly or through linkage with other information. Its boundaries depend on context and applicable rules, including how combinations of seemingly ordinary details can reveal identity.; Rights, privacy & control
Pseudonymization: Pseudonymization replaces identifying information with substitutes while keeping a way to reconnect records using additional information. It can reduce exposure, but pseudonymized records can still be personal data and require protection.; Rights, privacy & control

R

Re-identification risk: Re-identification risk is the possibility of connecting transformed or apparently non-identifying records back to people or entities. It depends on remaining detail, outside information, recipient capabilities, and the conditions of access.; Rights, privacy & control
Redaction: Redaction removes or obscures selected information before disclosure. Effective digital redaction must address underlying text, metadata, attachments, and alternate copies; covering visible text is not always enough to remove it.; Rights, privacy & control
Reward hacking: Reward hacking occurs when an AI system achieves a high measured reward in a way that misses the intended objective. It exposes a gap between what designers want and what the reward or evaluation mechanism actually measures.; AI training, agents & evaluation
RL environment: A reinforcement learning environment is the setting an agent interacts with through actions and observations. It supplies state transitions and rewards or feedback, allowing behavior to be learned or evaluated over sequences of interaction.; AI training, agents & evaluation
RLHF data: RLHF data supplies human feedback used in reinforcement learning from human feedback. It often includes comparisons or rankings of model outputs, with instructions and context needed to understand what reviewers preferred and why.; AI training, agents & evaluation

S

Sim-to-real gap: The sim-to-real gap is the difference between conditions represented in a simulation and those encountered in the real world. Models or policies trained in simulation can fail when appearance, dynamics, sensors, or constraints differ.; Video, robotics & physical AI
Synthetic training data: Synthetic training data is generated rather than directly recorded from the target real-world activity. It can come from models, simulations, or rules, and needs validation for usefulness, diversity, errors, privacy, and rights.; AI training, agents & evaluation
System of record: A system of record is the designated authoritative source for a particular kind of business information. An organization can have several systems of record, each responsible for different entities, fields, or processes.; Business data & workflows

T

Teleoperation: Teleoperation is the control of a machine or robot by a human through an interface. Recorded observations and commands can provide demonstrations, but timing, hardware, control mappings, and outcome quality determine how those records can be used.; Video, robotics & physical AI
Third-party data rights: Third-party data rights are rights or permissions held by parties other than the business proposing a data license. Customer content, supplier material, licensed references, and individual privacy interests can limit what that business may grant.; Rights, privacy & control
Training-ready data: Training-ready data has been prepared and checked for a specified model-training workflow. Readiness depends on the task, format, quality, documentation, and permissions; it is not a universal certification that any dataset can train any model.; Data quality & preparation

U

Unstructured data: Unstructured data is information whose substantive content does not fit neatly into predefined table fields. Documents, free text, images, audio, and video can carry useful context, even when the files also have structured metadata.; Business data & workflows

V

Verifier: A verifier checks whether a result satisfies defined requirements. It may use rules, tests, environment state, or a model; the reliability of its checks determines what a passing result actually establishes.; AI training, agents & evaluation
Voice & likeness rights: Voice and likeness rights concern uses of a person’s recognizable voice, image, or identity. Applicable protections and permissions vary by jurisdiction and can be separate from copyright in the recording itself.; Rights, privacy & control

W

Workflow data: Workflow data describes how work is organized and performed, including tasks, handoffs, states, decisions, and outcomes. It may span several systems and can include both structured events and supporting documents or messages.; Business data & workflows
Workflow trajectory: A workflow trajectory is an ordered record of a particular task’s progression through states, actions, and outcomes. It captures one execution path, including branches or errors, rather than merely describing how a process is supposed to work.; Business data & workflows
World model: A world model is a learned representation of an environment and how it may change. It can support prediction, planning, or simulated experience, but useful-looking predictions do not guarantee accurate physical behavior or reliable action outcomes.; Video, robotics & physical AI

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