AI System

Any software developed with machine‑learning, logic‑ and knowledge‑based, or statistical approaches that, for given sets of human‑defined objectives, generates outputs such as content, predictions, recommendations or decisions. The draft adopts this working meaning from Article 3(1) of the EU AI Act.

An AI system refers broadly to algorithmic systems, models or software that perform tasks using data and automated decision‑making across their lifecycle, encompassing stages from research and development through deployment and maintenance.

A system that uses computational techniques (e.g., ML, perception, NLP) with some degree of autonomy to generate outputs such as content, predictions, recommendations or decisions affecting persons or processes. The statute uses this term to set the regulatory scope for providers, importers, distributors and deployers operating within Argentine territory.

Any system that uses learning, reasoning or optimisation techniques to perform tasks that would otherwise require human intelligence; intended as a high‑level, cross‑sector definition for applying the Principles proportionally across use cases.

A software or service that performs tasks conventionally associated with human intelligence using models, algorithms and data-driven approaches, including machine learning and inferencing components used to automate or assist decision-making and actions.

The AIAF defines an 'AI system' as any software, service, model or pipeline that includes AI components and performs tasks requiring human‑like capabilities such as learning, reasoning, prediction or decision‑making; such systems are subject to assessment, documentation, testing and monitoring under the Framework.

A defined AI system or AI solution is software or processes using machine learning, statistical models or other computational techniques to produce outputs that inform, influence or automate decisions or actions. The term is used to determine whether the AIAF and related controls apply to a given project or system.

Adopts the OECD definition: machine-based systems that, for explicit or implicit objectives, infer from inputs how to generate outputs (predictions, content, recommendations, decisions) that can influence physical or virtual environments. The policy notes levels of autonomy and adaptiveness and recommends agencies keep definitions under review as the regulatory environment evolves.

A software-based tool or collection of components that generates outputs such as predictions, content, or recommendations from inputs; encompasses runtime behavior, interfaces and integrated models, and is the object of VAISS guardrails for design, deployment and oversight.

Adopting the OECD formulation, an 'AI system' is a machine-based system that, given data and objectives, infers, processes or models to generate outputs such as predictions, content, recommendations, or decisions; the Plan explicitly notes differing levels of autonomy and adaptiveness across development, deployment and post-deployment phases.

A software (and possibly hardware) system that, given a complex goal, acts in physical or digital environments by acquiring data, interpreting information and making or supporting decisions. The draft uses this working taxonomy to scope obligations and applicability across sectors.

A software system designed to operate with a degree of autonomy, adaptiveness and inference capability, able to perform tasks that would ordinarily require human intelligence; the Act uses this operational definition (Art. 3) to determine scope and obligations for providers, deployers and operators.

Any software that performs tasks commonly associated with human cognitive functions, including machine learning, rule-based algorithms and other models; defined functionally to be technology-neutral to accommodate evolving model types and deployment modes.

An automated system that, for explicit or implicit objectives, infers how to generate outputs such as predictions, content, recommendations or decisions that may affect environments; the statement adopts the OECD Council formulation to describe covered technologies and functions.

Software that, by processing input data using techniques such as machine‑learning, logic‑based or statistical approaches, produces outputs (e.g., predictions, recommendations, decisions) for a given set of tasks; the legal definition covers the software and components required for its operation in scope of the Regulation.

An integrated set of components (models, datasets, interfaces and updating mechanisms) that accepts data inputs and produces automated or semi‑automated outputs, including continuous update/learning mechanisms and related infrastructure. The bill uses this term to describe the regulated object subject to documentation, testing, registration and incident reporting obligations.

Any software or algorithmic system that generates outputs (predictions, recommendations, decisions or content) used to inform, recommend or make decisions or actions in a given context. The term covers systems across their lifecycle, including models, datasets and deployed instances that produce user‑facing outputs.

A machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions.

A concrete deployed system or model used for decision‑support or automation, encompassing operationalized models (e.g., ML, NLP, computer vision) that perform tasks in real‑world contexts and are subject to operational obligations under the Policy.

Software developed using machine‑learning, logic‑and‑knowledge‑based or statistical approaches that, for a given set of human‑defined objectives, generates outputs such as content, predictions, recommendations or decisions. The framework adopts this definition verbatim from Regulation (EU) 2024/1689 to determine the scope of regulated systems.

Technical systems that implement AI methods, including algorithmic models, machine learning and data‑driven systems; encompasses software and integrated systems that perform tasks relying on data‑driven inference and decision‑making.

A machine-based system that, for explicit or implicit objectives, infers from input data, adapts its behaviour, or generates outputs such as content, predictions, recommendations or decisions. The definition sets the scope for covered technologies and distinguishes systems by their capabilities and intended purpose within the Act.

Any software, model or processing pipeline that performs algorithmic decision‑making or inference within a project, including trained models, inference services and integrated AI components subject to the Action Plan's documentation and oversight requirements.

A machine-based system that generates outputs such as predictions, recommendations or decisions for human-defined objectives; the guidance adopts this practical OECD/EU-aligned formulation to frame data protection obligations across AI lifecycles.

In the draft directive, an AI system covers software, algorithms or systems that produce outputs (including content, predictions, recommendations or decisions) and is defined consistently with the AI Act; it is the object whose outputs may be implicated in harmful outcomes for which liability and evidentiary rules apply.

As used in the Refresh (aligned with the EU AI Act), an "AI system" means a machine-based system that, given input, produces outputs such as predictions, content, recommendations or decisions; this covers software, models and components across the AI lifecycle used to automate or augment tasks.

An AI System is a software or integrated system designed to operate with some level of autonomy and that, for explicit or implicit objectives, generates outputs such as predictions, recommendations, or decisions that influence physical or virtual environments. The strategy frames this concept in line with the EU AI Act and emphasizes the system-level scope (models, components, and deployments) relevant to regulation, development, and deployment within the region.

A machine-based system that, for a given set of human-defined objectives, infers how to achieve those objectives by learning, reasoning, or perceiving; used in the policy to scope covered AI technologies, including generative models and other deep‑tech applications.

Software, hardware or combined systems that produce outputs by machine learning or algorithmic means, defined operationally in line with NAIO and international practice and covered by MAIC requirements for documentation, testing and assessment.

A legally defined unit comprising models, training and operational data, and software components that together perform intellectual or task‑oriented functions; treated as the primary regulated object for classification, documentation, and obligations under the law.

A software or system that uses statistical, probabilistic, or machine‑learning methods to produce recommendations, predictions, or decisions for use by people or organisations. The term is defined pragmatically to support sectoral adaptation and interoperability with existing law.

Refers to software and systems that process data to generate predictions, recommendations or automated decisions; the strategy frames this broadly to include machine learning, expert systems, symbolic AI, statistical/probabilistic models and emerging generative systems for the purposes of governance and oversight.

Systems that perform data‑driven inference, prediction or decision‑support using models or algorithmic processes capable of learning or adapting. The guidance uses this broad operational definition to capture diverse algorithmic components used across government services, procurement and analytics.

A practical working definition aligned to the OECD: a machine‑based system that infers from input how to generate outputs (predictions, content, recommendations, decisions) that influence environments. The Framework distinguishes AI by capability (from simple automation to adaptive systems) and treats generative AI as a subset addressed by companion guidance.

A machine-based system that infers from input data to generate outputs such as predictions, recommendations or content; it encompasses models, components and operational pipelines used across design, deployment and monitoring stages of the AI lifecycle.

The technically assembled model together with its data pipelines and runtime environment that implements AI functionality; it encompasses trained models, input/output processing, deployment infrastructure and related components used to produce automated outputs or decisions.

Any software, model, or integrated system that performs tasks exhibiting human‑like cognitive functions (e.g., learning, reasoning, perception) and operates with varying levels of autonomy; used to describe the unit of deployment, assessment, and oversight under the policy.

An AI system is defined broadly to include machine learning models, generative models, and automated decision systems used to create, transform, or manipulate content; this definition scopes which technologies are subject to labeling, recordkeeping, and oversight obligations.

Any software, model, or platform that performs tasks with varying degrees of autonomy using data inputs, including systems that make inferences, provide recommendations, or automate decisions across applications and sectors. The NAIS draft uses this term to capture the range of algorithmic systems subject to strategy measures.

A combination of software and, where applicable, hardware designed to perform automated decision-making or generate outputs such as predictions, recommendations, or content; the draft frames this term to determine the scope of obligations (e.g., transparency, testing, cybersecurity) applicable to developers and users.

A system defined in the annex that implements data-driven models or software to perform tasks and make inferences, with the working definition tailored to be broadly aligned with EU AI definitions while allowing sectoral adaptation for national coordination.

Any system that, using computational models, produces outputs such as content, predictions, recommendations or decisions; the Strategy adopts this broad definition to align with EU instruments and enable cross-sector coordination. It is used as the principal object of policy measures, testing and public deployments in the Strategy.

Any machine-based system that uses artificial intelligence to generate outputs including predictions, recommendations, decisions, or content, whether operating autonomously or with human oversight; this encompasses software applications, embedded systems in physical devices, cloud-based AI services, and hybrid architectures combining multiple AI components.

Any system employing methods that gather, process, or analyze data to predict, suggest, or make decisions with varying degrees of autonomy across its operation. The definition scopes covered technologies by their function (prediction/decision-support/automation) rather than specific algorithms or models.

A functionally framed socio-technical system (per OECD/EU terminology) encompassing methods and components that provide capabilities for perception, reasoning, learning and decision‑making in specified contexts of use. The programme uses this operational definition to scope measures, reference solutions and sectoral pilots.

An apparatus, software or service that incorporates machine learning models or related algorithmic components to assist, recommend, predict or make decisions about individuals or groups. The Guidelines use this term to identify the PDPA obligations that attach when such systems process personal data during development, testing or deployment.

An integrated combination of models, software, data, and hardware components that together produce outputs such as predictions, recommendations, content or decisions. The definition encompasses lifecycle elements including development, deployment, monitoring and governance artifacts required for responsible use in government contexts.

A system composed of models, training and inference data, compute resources and the operational/deployment context that together perform tasks that normally require human intelligence; includes both the technical components (models, datasets, infrastructure) and the environment in which they are used.

A system that produces automated outputs (predictions, recommendations, decisions, or other content) through algorithmic processing, including machine learning, statistical methods and other automated decision‑making techniques; encompasses software and services that generate automated outputs used in business operations.

A software and/or hardware system that performs tasks commonly associated with human intelligence by processing data via algorithms, machine‑learning models or other automated reasoning techniques. The Guideline uses this term to cover components throughout an AI lifecycle, including models, data pipelines and deployed services.

A software application or automated decision‑support system that performs tasks that would otherwise require human intelligence, including models, algorithms, and related data pipelines used to generate outputs or decisions in a specific use case.

Systems that emulate human intelligence to learn, memorise, decide, operate and generate new content by learning from data, explicitly including machine learning, deep learning, generative models (such as large language models) and agentic systems, and explicitly excluding simple rule‑based automation (e.g., robotic process automation) and pre‑defined condition‑matching automation.

An integrated set of algorithms, models, software components and associated data that perform tasks or provide outputs by processing input data; the Strategy's definition covers machine learning models, rule‑based components, and their operational deployment within public or private services. AI systems are described with reference to their components, data sources, and lifecycle stages to harmonize terminology across agencies.

An operational understanding of systems and their lifecycle stages (design, training, testing, deployment, monitoring, decommissioning) that process data to produce predictions, recommendations or automated decisions; the Guide notes that outputs or inferences (including inferred sensitive attributes) may themselves be personal data under applicable law.

A legal definition covering software, models and algorithmic assemblies that, with limited or no human intervention, process data to perform specific tasks (including techniques like machine learning, deep learning and neural networks) and produce outputs such as decisions, recommendations or content. The definition is purposive and reaches both fully automated and semi‑autonomous systems to clarify which systems and outputs are subject to the bill's obligations (e.g., labeling, takedown duties and dataset requirements).

Encompasses technologies developed directly by media entities, commissioned from third parties, or adopted from external providers that are used by journalists and media workers in professional activities, including content creation, verification, translation, moderation, personalization, and audience analytics.

A machine-based system that processes inputs to produce outputs such as predictions, content, recommendations, or decisions; the Guide adopts an OECD-aligned operational definition to cover systems managed in products or services across their lifecycle.

Computational systems that, given objectives, infer from input data to produce outputs (predictions, recommendations, content or decisions) that may influence physical or virtual environments. The definition aligns with OECD guidance and frames systems by purpose, inputs, outputs and potential real‑world impact.

A software application or a combination of software and hardware deployed to perform tasks that make, support or inform decisions in the employment context, including tools used for selection, evaluation, surveillance, task allocation, remuneration or termination. The bill uses this definition to determine scope and obligations for deployment, documentation and oversight.

A broadly defined system encompassing models, algorithms, and associated data pipelines that perform automated processing and produce outputs used for decision‑making or other automated actions. The definition covers both standalone software and integrated systems deployed by public or private actors, and is the basis for applicability of impact assessment, registry and documentation obligations.

Software, models and data pipelines deployed to deliver AI outputs; this encompasses trained models, associated code, and the data processing and inference components used in operation and decision‑making.

An AI system as defined following the EU Artificial Intelligence Act: software developed with machine learning, logic‑based, or statistical approaches that produces outputs such as content, predictions, recommendations or decisions. The term covers models and software components regardless of deployment context.

Software developed using machine learning, logic- and knowledge-based approaches, or statistical methods that produces an output such as a decision, recommendation, or prediction; used to determine the scope of obligations and oversight under the law.

Software developed with machine learning, logic‑based, or statistical approaches that, for given human‑defined objectives, generates outputs such as content, predictions, recommendations or decisions; the definition covers both model‑level components and system‑level deployments across sectors.

A technology‑neutral concept covering software and related components that, by processing data through statistical, probabilistic, logic‑based, or other computational methods, produces outputs (predictions, recommendations, decisions or other content) to achieve specified objectives. The term is used broadly across the Act to capture systems subject to its obligations regardless of specific technique.

Systems that perform tasks using algorithmic models, including automated decision‑making systems and generative models; this encompasses the models, datasets, tooling and runtime components that produce system outputs.

A software-based system, including models and model components, designed to operate with varying degrees of autonomy to produce outputs such as predictions, recommendations or decisions; the Regulation frames this term purposefully to capture a wide range of algorithmic tools and their components for regulatory coverage.

A computational assemblage of software and/or hardware that employs AI methods to perform tasks commonly associated with intelligent agents, including models, inference engines, and supporting infrastructure used to deliver AI-driven functionality.

An AI system is understood as the complete assemblage encompassing models, training and evaluation data, development methods, deployment lifecycle, monitoring, and human oversight mechanisms — i.e., not just code but datasets, processes and operational practices required for the system to function and be governed.

A machine-based system designed to operate with varying levels of autonomy that, for explicit or implicit goals, produces outputs such as content, predictions, recommendations or decisions which may affect environments humans interact with; the Act uses this definition to set the scope of covered technologies and obligations.

A machine‑based system which may exhibit adaptiveness and infers outputs from input data, including systems that generate content, recommendations, or decisions. The draft law adopts this EU AI Act definition to determine scope and applicable obligations.

A machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.

Software that uses techniques such as machine learning, logic‑ and knowledge‑based approaches and inference to generate outputs for given objectives; the core object of regulation under the Act and the unit to which obligations apply.

A machine-based system that, for explicit or implicit objectives, infers from the input it receives how to generate outputs—such as predictions, content, recommendations, or decisions—that can influence physical or virtual environments.

Any physical or virtual product or service that employs artificial intelligence methods to perform tasks or provide functionality to end users; the Guidance uses this term to cover systems throughout design, deployment and operation phases.

Automated or semi-automated systems that employ models to make predictions, recommendations, or decisions across operational contexts; encompasses deployed models, their inputs/outputs, and associated components across the AI lifecycle.

Software that uses statistical, machine‑learning, or other algorithmic approaches to produce outputs that influence decisions or actions; the working definition MAS uses to determine scope of coverage for the Guidelines.

An automated system employing algorithms or models to perform tasks that would normally require human intelligence. The definition is deliberately broad to encompass evolving model architectures and various application types across the AI lifecycle.

A functional system (software and/or hardware) that produces outputs such as predictions, recommendations, or decisions by implementing capabilities associated with artificial intelligence; the Act uses this term to capture deployed systems subject to obligations and oversight.

A machine-based system that, for explicit or implicit objectives, infers from the input it receives how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.

A technical system that automates or augments decision‑making, encompassing models, pipelines and associated data processing components used to produce outputs that inform or take actions affecting individuals or groups.

A software or machine-based system that uses data-driven models to perform tasks and that may iteratively improve over time; used here to refer to deployed or prospective automated decision‑making or assistance tools within government services.

An artificial intelligence system refers to a machine-based system that operates in physical or virtual environments, capable of generating content, making decisions, recommendations, or predictions. It includes various forms of AI, excluding basic procedural tools and cybersecurity software.

An AI system is a machine-based system that, for explicit or implicit objectives, infers from input how to generate outputs such as predictions, content, recommendations, or decisions capable of influencing physical or virtual environments. This definition aligns with the OECD definition adopted by the DTA for policy scope.

An AI system is a machine-based system that, for a given set of human-defined objectives, can make predictions, recommendations, or decisions influencing real or virtual environments. These systems operate with varying levels of autonomy and adapt through data analysis.

A machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments.

Technologies that integrate models and algorithms to produce capacity to learn and perform tasks like prediction and decision-making.

An engineered system that generates outputs such as content, forecasts, recommendations, or decisions for a given set of human-defined objectives.

An engineered system that generates outputs such as content, forecasts, recommendations, or decisions for a given set of human-defined objectives.

An AI-based system that, with varying levels of autonomy and adaptability, infers outcomes like predictions, recommendations, or decisions that influence real or virtual environments to achieve specified goals.

An AI system is defined as software utilizing techniques listed in Annex I of the EU AI Act, capable of generating outputs such as content, predictions, or decisions for human-defined objectives. These systems interact with and influence their environments, encompassing a broad range of AI applications.

Any software or system employing automated decision-making techniques that is classified into categories or risk tiers under the Framework, where higher-risk tiers trigger mandatory risk assessments, registration/notification, enhanced transparency, human-review rights and stricter conformity and audit requirements.

A machine-based system that, for explicit or implicit objectives, infers from the input it receives how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. Different AI systems vary in their levels of autonomy and adaptiveness after deployment.

Refers to an AI-based system that infers results such as predictions, recommendations, or decisions, influencing real or virtual environments with varying levels of autonomy and adaptability, establishing the technological boundaries for the Act.

An AI system is defined as a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. This definition aligns with broader OECD principles, focusing on functional aspects and potential impacts.

A machine-based system that is designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment, and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.

An 'AI system' is broadly defined as a machine-based system designed to operate with varying levels of autonomy, inferring from inputs to generate outputs that can influence physical or virtual environments.

According to the OECD, an AI system is a machine-based system designed to operate with varying levels of autonomy, which may exhibit adaptiveness after deployment. For explicit or implicit objectives, it infers from the input it receives how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.

A system based on machine learning and heuristic systems, whose life cycle processes are defined by this standard.

A system that utilizes artificial intelligence technology, often based on machine learning techniques, and may exhibit properties like adaptability and probabilistic behavior.

An AI system is defined as a machine-based system designed to operate with varying levels of autonomy. It infers from the input it receives how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. This broad definition covers a wide spectrum of AI technologies, from simple rule-based systems to complex deep learning models.

An AI system is generally defined as a machine-based system that, for explicit or implicit objectives, infers from the input it receives how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. This broad definition encompasses various forms of artificial intelligence.

According to Article 3 of the Act, an AI system refers to a system with autonomous operational capabilities. This system, through input or sensing, and via machine learning and algorithms, can achieve predictions, content, recommendations, or decisions for explicit or implicit objectives, producing outputs that affect physical or virtual environments.

A machine-based system that, for explicit or implicit objectives, infers from the input it receives how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. This broad definition captures a wide array of AI technologies and applications.

An AI system is a machine-based system that operates with varying levels of autonomy and can, for explicit or implicit objectives, generate outputs such as predictions, recommendations, or decisions that influence physical or virtual environments. It is defined broadly by the EU AI Act and adopted by the KI-MIG.

Refers to a machine-based system that, for explicit or implicit objectives, infers from the input it receives how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments, aligning with the EU AI Act.

A machine-based system that operates with varying levels of autonomy and can, for explicit or implicit objectives, generate outputs such as predictions, recommendations, or decisions influencing physical or virtual environments.

A machine-based system designed to operate with some level of autonomy that can adapt after it is deployed and generate outputs such as predictions, content, recommendations, or decisions from input it receives to achieve explicit or implicit objectives.

An AI system is a machine-based system designed to operate with varying levels of autonomy and that can, for explicit or implicit objectives, generate outputs such as predictions, recommendations, or decisions influencing physical or virtual environments. It is developed using machine learning, logic- and knowledge-based approaches, or statistical approaches.