AI Terms and Governance
Accountability
Accountability means named parties answer for AI system outcomes, evidence, and remedies.
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AI Terms and Governance Terms
High-risk AI, GPAI, oversight models, impact assessments, and algorithmic-governance vocabulary.
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66 published definitions in this topic.
AI Terms and Governance
Adversarial Testing
Adversarial testing evaluates AI systems against crafted inputs designed to cause errors or unsafe outputs.
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AI Assurance
AI assurance is independent or structured evidence that AI systems meet stated risk, ethics, and performance claims.
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AI Audit
An AI audit is an examination of an AI system’s controls, evidence, and outcomes against defined criteria.
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AI Deployer
An AI deployer is the entity that uses an AI system under its authority, except where the system is for personal non-professional activity.
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AI Developer
An AI developer is a person or organization that designs, trains, or builds AI models or systems.
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AI Distributor
An AI distributor makes an AI system available on the market without affecting its properties as provider.
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AI Importer
An AI importer is an entity established in a jurisdiction that places on the market an AI system from a third-country provider.
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AI Inventory
An AI inventory is a maintained register of AI systems and models an organization uses or provides.
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AI Literacy
AI literacy is the understanding staff and users need to use and oversee AI systems responsibly.
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AI Operator
An AI operator is an organization or team responsible for running and maintaining an AI system in production.
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AI Provider
An AI provider is the entity that develops an AI system or model and places it on the market or puts it into service under its name.
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AI Regulatory Sandbox
An AI regulatory sandbox is a supervised environment for testing AI systems with regulator support under defined safeguards.
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AI-Generated Content Label
An AI-generated content label is a visible or accessible notice that content was produced with AI.
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Algorithmic Audit
An algorithmic audit specifically inspects algorithms and automated decision systems for bias, accuracy, and compliance issues.
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Algorithmic Discrimination
Algorithmic discrimination is unjustified unfavorable treatment produced or amplified by automated systems.
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Algorithmic Impact Assessment
An algorithmic impact assessment evaluates how an automated system may affect people, groups, and rights before and during use.
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Artificial Intelligence System
An artificial intelligence system is a machine-based system that infers how to generate outputs such as predictions, content, recommendations, or decisions from inputs.
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Authorized Representative
An authorized representative is a person or entity mandated to act on behalf of a non-local provider for regulatory purposes.
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Automated Decision System
An automated decision system is a system that classifies, scores, or decides about people or situations with significant automation.
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Automated Decision-Making
Automated decision-making is deciding about a person using automated means without meaningful human involvement in the specific decision.
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Bias Testing
Bias testing measures whether AI outputs systematically disadvantage or misrepresent groups or individuals unfairly.
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Conformity Assessment
Conformity assessment for AI is the process of demonstrating that an AI system meets applicable legal requirements.
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Contestability
Contestability is the ability for affected people to challenge an automated or AI-assisted decision and obtain review.
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Deepfake Disclosure
Deepfake disclosure is a specific notice that media depicting a person was synthetically generated or manipulated.
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Explainability
Explainability is the ability to provide understandable reasons for an AI system’s outputs or behavior.
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Fairness
Fairness in AI is the goal that systems treat people justly without unjustified disparate harms.
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Foundation Model
A foundation model is a large model trained on broad data that serves as a base for many specialized applications.
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Frontier Model
A frontier model is an informal label for the most capable, cutting-edge AI models at a given time.
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Fundamental Rights Impact Assessment
A fundamental rights impact assessment analyzes how an AI system may affect rights such as dignity, equality, privacy, and due process.
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General-Purpose AI
General-purpose AI (GPAI) refers to AI models or systems capable of competently performing a wide range of distinct tasks.
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General-Purpose AI Model
A general-purpose AI model is a model trained on broad data that can be adapted to many downstream tasks.
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Harmful Bias
Harmful bias is systematic model or data skew that produces unjustified adverse effects on people or groups.
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High-Impact AI System
A high-impact AI system is an AI system whose outputs can significantly affect people’s rights, safety, or opportunities.
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High-Risk AI System
A high-risk AI system is an AI system that falls into legally defined high-risk categories—especially under the EU AI Act—triggering stricter duties.
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Human Oversight
Human oversight means designing AI use so people can understand, monitor, and intervene in system operation appropriately.
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Human-in-Command
Human-in-command means humans retain ultimate authority to set goals, constraints, and override AI systems.
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Human-in-the-Loop
Human-in-the-loop means a human must actively approve or supply input before the AI output takes effect.
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Human-on-the-Loop
Human-on-the-loop means humans monitor AI operation and can intervene, without approving every single output in advance.
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Intended Purpose
Intended purpose is the use for which an AI system is designed and offered by the provider, including stated context and users.
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Interpretability
Interpretability is the degree to which a human can understand the internal mechanics of how a model works.
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Limited-Risk AI System
A limited-risk AI system is AI that mainly triggers transparency duties rather than the full high-risk regime.
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Machine Learning System
A machine learning system is software that learns patterns from data to improve performance on a task without being fully hard-coded for every case.
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Machine-Readable Disclosure
Machine-readable disclosure embeds AI or provenance information in formats software can detect automatically.
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Model Card
A model card is a structured disclosure document describing an AI model’s intended use, data, metrics, and limits.
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Model Evaluation
Model evaluation is systematic testing of an AI model’s performance, safety, and behavior against defined criteria.
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Model Risk
Model risk is the potential for adverse consequences from incorrect or misused model outputs.
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Post-Market Monitoring
Post-market monitoring is ongoing surveillance of an AI system’s performance and risks after it is deployed.
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Profiling
Profiling is automated processing of personal data to evaluate personal aspects such as performance, preferences, or risks.
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Prohibited AI Practice
A prohibited AI practice is an AI use that applicable law bans outright rather than merely regulating.
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Reasonably Foreseeable Misuse
Reasonably foreseeable misuse is incorrect use that a provider can anticipate based on human behavior and product design.
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Red Teaming
Red teaming is adversarial testing where specialists try to make an AI system fail, leak, or behave unsafely.
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Responsible AI
Responsible AI is organizational practice that manages AI risks to people and society throughout the lifecycle.
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Right to Explanation
A right to explanation is a legal or policy entitlement to meaningful information about an automated decision’s basis.
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Robustness
Robustness is an AI system’s ability to maintain acceptable performance under shift, noise, or attack.
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Serious Incident Reporting
Serious incident reporting is the duty to notify authorities about AI-related incidents that cause or could cause serious harm.
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Substantial Modification
A substantial modification is a change to an AI system that affects compliance or intended purpose enough to trigger new provider duties.
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Synthetic Content Disclosure
Synthetic content disclosure means informing people when content was generated or manipulated by AI.
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System Card
A system card is a disclosure describing an AI system’s end-to-end behavior, integrations, and safeguards—not only the base model.
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Systemic Risk
Systemic risk is the risk of large-scale negative effects across markets, public infrastructure, or society from AI models or uses.
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