The OECD Framework for the Classification of AI systems
The OECD Framework for the Classification of AI Systems helps characterise AI systems
to identifiy policy opportunities and challenges
AI changes how people learn, work, play, interact and live. As AI spreads across sectors, different typesof AI systems deliver different benefits, risks and policy and regulatory challenges. Consider the differences
between a virtual assistant, a self-driving vehicle and an algorithm that recommends videos for children.
The OECD developed a user-friendly framework for policy makers, regulators, legislators and others tocharacterise AI systems for specific projects and contexts. The framework links AI system characteristics
with the OECD AI Principles (OECD, 2019), the first set of AI standards that governments pledged toincorporate into policy making and promote the innovative and trustworthy use of AI.

Key dimensions structure AI system characteristics and interactions
The framework classifies AI systems and applications along the following dimensions: People & Planet,
Economic Context, Data & Input, AI Model and Task & Output. Each one has its own properties andattributes or sub-dimensions relevant to assessing policy considerations of particular AI systems.

Key dimensions of the OECD Framework for the Classification of AI Systems

Ways to use the framework
The framework allows users to zoom in on specific risks that are typical of AI, such as bias, explainability
and robustness, yet it is generic in nature. It facilitates nuanced and precise policy debate. The framework
can also help develop policies and regulations, since AI system characteristics influence the technical andprocedural measures they need for implementation. In particular, the framework provides a baseline
framework to help support and advance:•A common understanding of AI, and metrics•

Registries or inventories of AI systems•

Sector-specific frameworks, e.g. in healthcare•

Risk assessment, incident reporting and risk management

1© OECD 2022

Classification framework dimensions and criteria at a glance
PEOPLE & PLANET

Criteria

Description

USERS
STAKEHOLDERSOPTIONALITY
HUMAN RIGHTS

Users of AI system
Impacted stakeholders
Optionality and redress
Human rights and democratic values

WELL-BEING &ENVIRONMENTDISPLACEMENT

Well-being, society and theenvironment{Displacement potential}

What is the level of competency of users who interact with the system?
Who is impacted by the system (e.g. consumers, workers, government agencies)?
Can users opt out, e.g. switch systems? Can users challenge or correct the output?
Can the system’s outputs impact fundamental human rights (e.g. human dignity,privacy, freedom of expression, non-discrimination, fair trial, remedy, safety)?
Can the system’s outputs impact areas of life related to well-being (e.g. job quality,
the environment, health, social interactions, civic engagement, education)?
Could the system automate tasks that are or were being executed by humans?

ECONOMIC CONTEXT

Criteria

SECTOR
Industrial sector
BUSINESS FUNCTION & Business function
MODEL
Business model
CRITICALITY
Impacts critical functions / activities
Breadth of deployment
SCALE & MATURITY{Technical maturity}

DATA & INPUT

Criteria
Detection and collection
Provenance of data and input
Dynamic nature
RIGHTS &Rights
IDENTIFIABILITY“Identifiability” of personal data
{Structure of data and input}
STRUCTURE & FORMAT{Format of data and metadata}
SCALE{Scale}QUALITY AND{Data quality and appropriateness}
APPROPRIATENESSCOLLECTION

AI MODEL
MODEL
CHARACTERISTICS

MODEL-BUILDINGMODEL INFERENCETASK & OUTPUTTASKSACTIONAPPLICATION AREAEVALUATION

Description
Which industrial sector is the system deployed in (e.g. finance, agriculture)?
What business function(s) is the system employed in (e.g. sales, customer service)?
Is the system a for-profit use, non-profit use or public service system?
Would a disruption of the system’s function / activity affect essential services?
Is the AI system deployment a pilot, narrow, broad or widespread?
How technically mature is the system (Technology Readiness Level –TRL)
Description
Are the data and input collected by humans, automated sensors or both?
Are the data and input from experts; provided, observed, synthetic or derived?
Are the data dynamic, static, dynamic updated from time to time or real-time?
Are the data proprietary, public or personal data (related to identifiable individual)?
If personal data, are they anonymised; pseudonymised?
Are the data structured, semi-structured, complex structured or unstructured?
Is the format of the data and metadata standardised or non-standardised?
What is the dataset’s scale?
Is the dataset fit for purpose? Is the sample size adequate? Is it representative andcomplete enough? How noisy are the data?

Criteria
Model information availability
AI model type
{Rights associated with model}{Discriminative or generative}{Single or multiple model(s)}Model-building from machine or
human knowledge
Model evolution in the field ML
Central or federated learning ML{Model development / maintenance}{Deterministic and probabilistic}
Transparency and explainability

Description
Is any information available about the system’s model?
Is the model symbolic (human-generated rules), statistical (uses data) or hybrid?
Is the model open-source or proprietary, self or third-party managed?
Is the model generative, discriminative or both?
Is the system composed of one model or several interlinked models?
Does the system learn based on human-written rules, from data, through supervised
learning, through reinforcement learning?
Does the model evolve and / or acquire abilities from interacting with data in the field?
Is the model trained centrally or in a number of local servers or “edge” devices?
Is the model universal, customisable or tailored to the AI actor’s data?
Is the model used in a deterministic or probabilistic manner?
If information available to users to allow them to understand model outputs?

Criteria
Task(s) of the system{Combining tasks and actions into
composite systems}
Action autonomy
Core application area(s)Description
What tasks does the system perform (e.g. recognition, event detection, forecasting)?
Does the system combine several tasks and actions (e.g. content generation
systems, autonomous systems, control systems)?
How autonomous are the system’s actions and what role do humans play?
Does the system belong to a core application area such as human language
technologies, computer vision, automation and / or optimisation or robotics?
Are standards or methods available for evaluating system output?

{Evaluation methods}

Note: Criteria and descriptions in grey and marked with an {} symbol = those where objective and consistent information is available. ML = formachine learning AI models.

More information: www.oecd.ai/classification | Contact: ai@oecd.org
2© OECD 2022