ISO/IEC 22989:2022

INTERNATIONAL ISO/IEC
STANDARD 22989
First edition
2022-07
Information technology — Artificial
intelligence — Artificial intelligence
concepts and terminology
Technologies de l'information — Intelligence artificielle — Concepts
et terminologie relatifs à l'intelligence artificielle
Reference number
© ISO/IEC 2022
© ISO/IEC 2022
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Contents Page
Foreword . vi
Introduction .vii
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
3.1 Terms related to AI . 1
3.2 Terms related to data. 6
3.3 Terms related to machine learning . 8
3.4 Terms related to neural networks . 10
3.5 Terms related to trustworthiness . 11
3.6 Terms related to natural language processing . 13
3.7 Terms related to computer vision . 16
4 Abbreviated terms .16
5 AI concepts .17
5.1 General . 17
5.2 From strong and weak AI to general and narrow AI . 17
5.3 Agent . 17
5.4 Knowledge . 18
5.5 Cognition and cognitive computing . 19
5.6 Semantic computing . 19
5.7 Soft computing . 19
5.8 Genetic algorithms . 19
5.9 Symbolic and subsymbolic approaches for AI . 19
5.10 Data . 20
5.11 Machine learning concepts . 21
5.11.1 Supervised machine learning . 21
5.11.2 Unsupervised machine learning . 21
5.11.3 Semi-supervised machine learning . 22
5.11.4 Reinforcement learning .22
5.11.5 Transfer learning . 22
5.11.6 Training data . .22
5.11.7 Trained model .22
5.11.8 Validation and test data . 22
5.11.9 Retraining .23
5.12 Examples of machine learning algorithms . 24
5.12.1 Neural networks . 24
5.12.2 Bayesian networks . 25
5.12.3 Decision trees . 25
5.12.4 Support vector machine .25
5.13 Autonomy, heteronomy and automation . 26
5.14 Internet of things and cyber-physical systems . 27
5.14.1 General . 27
5.14.2 Internet of things . 27
5.14.3 Cyber-physical systems . 27
5.15 Trustworthiness .28
5.15.1 General .28
5.15.2 AI robustness . .28
5.15.3 AI reliability .29
5.15.4 AI resilience . 29
5.15.5 AI controllability .29
5.15.6 AI explainability . .29
5.15.7 AI predictability .30
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5.15.8 AI transparency .30
5.15.9 AI bias and fairness .30
5.16 AI verification and validation . . 31
5.17 Jurisdictional issues . 31
5.18 Societal impact . 32
5.19 AI stakeholder roles . 32
5.19.1 General . 32
5.19.2 AI provider . 33
5.19.3 AI producer . 33
5.19.4 AI customer .34
5.19.5 AI partner .34
5.19.6 AI subject .34
5.19.7 Relevant authorities . 35
6 AI system life cycle .35
6.1 AI system life cycle model . 35
6.2 AI system life cycle stages and processes . 37
6.2.1 General . 37
6.2.2 Inception . 37
6.2.3 Design and development .38
6.2.4 Verification and Validation . 39
6.2.5 Deployment . 39
6.2.6 Operation and monitoring . 39
6.2.7 Continuous validation .40
6.2.8 Re-evaluation .40
6.2.9 Retirement .40
7 AI system functional overview .40
7.1 General .40
7.2 Data and information . . 41
7.3 Knowledge and learning . 41
7.4 From predictions to actions . . 42
7.4.1 General . 42
7.4.2 Prediction . 42
7.4.3 Decision . 43
7.4.4 Action . 43
8 AI ecosystem .43
8.1 General . 43
8.2 AI systems. 45
8.3 AI function . 45
8.4 Machine learning . 45
8.4.1 General . 45
8.5 Engineering .46
8.5.1 General .46
8.5.2 Expert systems .46
8.5.3 Logic programming .46
8.6 Big data and data sources — cloud and edge computing .46
8.6.1 Big data and data sources.46
8.6.2 Cloud and edge computing .48
8.7 Resource pools .50
8.7.1 General .50
8.7.2 Application-specific integrated circuit .50
9 Fields of AI.51
9.1 Computer vision and image recognition. 51
9.2 Natural language processing . 51
9.2.1 General . 51
9.2.2 Natural language processing components . 52
9.3 Data mining .54
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9.4 Planning .54
10 Applications of AI systems .54
10.1 General .54
10.2 Fraud detection . 55
10.3 Automated vehicles .55
10.4 Predictive maintenance.56
Annex A (informative) Mapping of the AI system life cycle with the OECD’s definition of an
AI system life cycle .57
Bibliography .59
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Foreword
ISO (the International Organization for Standardization) and IEC (the International Electrotechnical
Commission) form the specialized system for worldwide standardization. National bodies that are
members of ISO or IEC participate in the development of International Standards through technical
committees established by the respective organization to deal with particular fields of technical
activity. ISO and IEC technical committees collaborate in fields of mutual interest. Other international
organizations, governmental and non-governmental, in liaison with ISO and IEC, also take part in the
work.
The procedures used to develop this document and those intended for its further maintenance are
described in the ISO/IEC Directives, Part 1. In particular, the different approval criteria needed for
the different types of document should be noted. This document was drafted in accordance with the
editorial rules of the ISO/IEC Directives, Part 2 (see www.iso.org/directives or www.iec.ch/members_
experts/refdocs).
Attention is drawn to the possibility that some of the elements of this document may be the subject
of patent rights. ISO and IEC shall not be held responsible for identifying any or all such patent
rights. Details of any patent rights identified during the development of the document will be in the
Introduction and/or on the ISO list of patent declarations received (see www.iso.org/patents) or the IEC
list of patent declarations received (see patents.iec.ch).
Any trade name used in this document is information given for the convenience of users and does not
constitute an endorsement.
For an explanation of the voluntary nature of standards, the meaning of ISO specific terms and
expressions related to conformity assessment, as well as information about ISO's adherence to the
World Trade Organization (WTO) principles in the Technical Barriers to Trade (TBT) see www.iso.org/
iso/foreword.html. In the IEC, see www.iec.ch/understanding-standards.
This document was prepared by Joint Technical Committee ISO/IEC JTC 1, Information technology,
Subcommittee SC 42, Artificial Intelligence.
Any feedback or questions on this document should be directed to the user’s national standards body. A
complete listing of these bodies can be found at www.iso.org/members.html and www.iec.ch/national-
committees.
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Introduction
Advancements in computing capacity, reduction of costs of computation, availability of large amounts
of data from many sources, inexpensive online learning curricula and algorithms capable of meeting or
exceeding human level performance in particular tasks for speed and accuracy have enabled practical
applications of AI, making it an increasingly important branch of information technology.
AI is a highly interdisciplinary field broadly based on computer science, data science, natural sciences,
humanities, mathematics, social sciences and others. Terms such as “intelligent”, “intelligence”,
“understanding”, “knowledge”, “learning”, “decisions”, “skills”, etc. are used throughout this document.
However, it is not the intention to anthropomorphize AI systems, but to describe the fact that some AI
systems can rudimentarily simulate such characteristics.
There are many areas of AI technology. These areas are intricately linked and developing rapidly so it is
difficult to fit the relevance of all technical fields into a single map. Research of AI includes aspects such
as aspects including “learning, recognition and prediction”, “inference, knowledge and language” and
[23]
“discovery, search and creation”. Research also addresses interdependencies among these aspects .
The concept of AI as an input and output process flow is shared by many AI researchers, and research on
each step of this process is ongoing. Standardized concepts and terminology are needed by stakeholders
of the technology to be better understood and adopted by a broader audience. Furthermore, concepts
and categories of AI allow for a comparison and classification of different solutions with respect
to properties like trustworthiness, robustness, resilience, reliability, accuracy, safety, security and
privacy. This enables stakeholders to select appropriate solutions for their applications and to compare
the quality of available solutions on the market.
As this document does provide a definition for the term AI in the sense of a discipline only, the context
for its usage can be described as follows: AI is a technical and scientific field devoted to the engineered
system that generates outputs such as content, forecasts, recommendations or decisions for a given set
of human-defined objectives.
This document provides standardized concepts and terminology to help AI technology to be better
understood and used by a broader set of stakeholders. It is intended for a wide audience including
experts and non-practitioners. The reading of some specific clauses can however be easier with a
stronger background in computer science. These concerns are described primarily Clauses 5.10, 5.11
and 8, which are more technical than the rest of the document.
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INTERNATIONAL STANDARD ISO/IEC 22989:2022(E)
Information technology — Artificial intelligence —
Artificial intelligence concepts and terminology
1 Scope
This document establishes terminology for AI and describes concepts in the field of AI.
This document can be used in the development of other standards and in support of communications
among diverse, interested parties or stakeholders.
This document is applicable to all types of organizations (e.g. commercial enterprises, government
agencies, not-for-profit organizations).
2 Normative references
There are no normative references in this document.
3 Terms and definitions
For the purposes of this document, the following terms and definitions apply.
ISO and IEC maintain terminology databases for use in standardization at the following addresses:
— ISO Online browsing platform: available at https:// www .iso .org/ obp
— IEC Electropedia: available at https:// www .electropedia .org/
3.1 Terms related to AI
3.1.1
AI agent
automated (3.1.7) entity that senses and responds to its environment and takes actions to achieve its
goals
3.1.2
AI component
functional element that constructs an AI system (3.1.4)
3.1.3
artificial intelligence
AI
research and development of mechanisms and applications of AI systems (3.1.4)
Note 1 to entry: Research and development can take place across any number of fields such as computer science,
data science, humanities, mathematics and natural sciences.
3.1.4
artificial intelligence system
AI system
engineered system that generates outputs such as content, forecasts, recommendations or decisions for
a given set of human-defined objectives
Note 1 to entry: The engineered system can use various techniques and approaches related to artificial intelligence
(3.1.3) to develop a model (3.1.23) to represent data, knowledge (3.1.21), processes, etc. which can be used to
conduct tasks (3.1.35).
© ISO/IEC 2022 – All rights reserved

Note 2 to entry: AI systems are designed to operate with varying levels of automation (3.1.7).
3.1.5
autonomy
autonomous
characteristic of a system that is capable of modifying its intended domain of use or goal without
external intervention, control or oversight
3.1.6
application specific integrated circuit
ASIC
integrated circuit customized for a particular use
[SOURCE: ISO/IEC/IEEE 24765:2017, 3.193, modified — Acronym has been moved to separate line.]
3.1.7
automatic
automation
automated
pertaining to a process or system that, under specified conditions, functions without human
intervention
[SOURCE: ISO/IEC 2382:2015, 2121282, modified — In the definition, “a process or equipment” has
been replaced by “a process or system” and preferred terms of “automated and automation” are added.]
3.1.8
cognitive computing
category of AI systems (3.1.4) that enables people and machines to interact more naturally
Note 1 to entry: Cognitive computing tasks are associated with machine learning (3.3.5), speech processing,
natural language processing (3.6.9), computer vision (3.7.1) and human-machine interfaces.
3.1.9
continuous learning
continual learning
lifelong learning
incremental training of an AI system (3.1.4) that takes place on an ongoing basis during the operation
phase of the AI system life cycle
3.1.10
connectionism
connectionist paradigm
connectionist model
connectionist approach
form of cognitive modelling that uses a network of interconnected units that generally are simple
computational units
3.1.11
data mining
computational process that extracts patterns by analysing quantitative data from different perspectives
and dimensions, categorizing them, and summarizing potential relationships and impacts
[SOURCE: ISO 16439:2014, 3.13, modified — replace “categorizing it” with “categorizing them” because
data is plural.]
3.1.12
declarative knowledge
knowledge represented by facts, rules and theorems
Note 1 to entry: Usually, declarative knowledge cannot be processed without first being translated into procedural
knowledge (3.1.28).
© ISO/IEC 2022 – All rights reserved

[SOURCE: ISO/IEC 2382-28:1995, 28.02.22, modified — Remove comma after “rules” in the definition.]
3.1.13
expert system
AI system (3.1.4) that accumulates, combines and encapsulates knowledge (3.1.21) provided by a human
expert or experts in a specific domain to infer solutions to problems
3.1.14
general AI
AGI
artificial general intelligence
type of AI system (3.1.4) that addresses a broad range of tasks (3.1.35) with a satisfactory level of
performance
Note 1 to entry: Compared to narrow AI (3.1.24).
Note 2 to entry: AGI is often used in a stronger sense, meaning systems that not only can perform a wide variety
of tasks, but all tasks that a human can perform.
3.1.15
genetic algorithm
GA
algorithm which simulates natural selection by creating and evolving a population of individuals
(solutions) for optimization problems
3.1.16
heteronomy
heteronomous
characteristic of a system operating under the constraint of external intervention, control or oversight
3.1.17
inference
reasoning by which conclusions are derived from known premises
Note 1 to entry: In AI, a premise is either a fact, a rule, a model, a feature or raw data.
Note 2 to entry: The term "inference" refers both to the process and its result.
[SOURCE: ISO/IEC 2382:2015, 2123830, modified – Model, feature and raw data have been added.
Remove “Note 4 to entry: 28.03.01 (2382)”. Remove “Note 3 to entry: inference: term and definition
standardized by ISO/IEC 2382-28:1995”.]
3.1.18
internet of things
IoT
infrastructure of interconnected entities, people, systems and information resources together with
services that process and react to information from the physical world and virtual world
[SOURCE: ISO/IEC 20924:2021, 3.2.4, modified – “…services which processes and reacts to…” has been
replaced with “…services that process and react to…” and acronym has been moved to separate line.]
3.1.19
IoT device
entity of an IoT system (3.1.20) that interacts and communicates with the physical world through
sensing or actuating
Note 1 to entry: An IoT device can be a sensor or an actuator.
[SOURCE: ISO/IEC 20924:2021, 3.2.6]
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3.1.20
IoT system
system providing functionalities of IoT (3.1.18)
Note 1 to entry: An IoT system can include, but not be limited to, IoT devices, IoT gateways, sensors and actuators.
[SOURCE: ISO/IEC 20924:2021, 3.2.9]
3.1.21
knowledge
abstracted information about objects, events, concepts or rules, their
relationships and properties, organized for goal-oriented systematic use
Note 1 to entry: Knowledge in the AI domain does not imply a cognitive capability, contrary to usage of the term
in some other domains. In particular, knowledge does not imply the cognitive act of understanding.
Note 2 to entry: Information can exist in numeric or symbolic form.
Note 3 to entry: Information is data that has been contextualized, so that it is interpretable. Data is created
through abstraction or measurement from the world.
3.1.22
life cycle
evolution of a system, product, service, project or other human-made entity, from conception through
retirement
[SOURCE: ISO/IEC/IEEE 15288:2015, 4.1.23]
3.1.23
model
physical, mathematical or otherwise logical representation of a system, entity, phenomenon, process or
data
[SOURCE: ISO/IEC 18023-1:2006, 3.1.11, modified – Remove comma after “mathematical” in the
definition. "or data" is added at the end.]
3.1.24
narrow AI
type of AI system (3.1.4) that is focused on defined tasks (3.1.35) to address a specific problem
Note 1 to entry: Compared to general AI (3.1.14).
3.1.25
performance
measurable result
Note 1 to entry: Performance can relate either to quantitative or qualitative findings.
Note 2 to entry: Performance can relate to managing activities, processes, products (including services), systems
or organizations.
3.1.26
planning
computational processes that compose a workflow out of a set of actions,
aiming at reaching a specified goal
Note 1 to entry: The meaning of the “planning” used in AI life cycle or AI management standards can be also
actions taken by human beings.
© ISO/IEC 2022 – All rights reserved

3.1.27
prediction
primary output of an AI system (3.1.4) when provided with input data (3.2.9) or information
Note 1 to entry: Predictions can be followed by additional outputs, such as recommendations, decisions and
actions.
Note 2 to entry: Prediction does not necessarily refer to predicting something in the future.
Note 3 to entry: Predictions can refer to various kinds of data analysis or production applied to new data or
historical data (including translating text, creating synthetic images or diagnosing a previous power failure).
3.1.28
procedural knowledge
knowledge which explicitly indicates the steps to be taken in order to solve a problem or to reach a goal
[SOURCE: ISO/IEC 2382-28:1995, 28.02.23]
3.1.29
robot
automation system with actuators that performs intended tasks (3.1.35) in the physical world, by
means of sensing its environment and a software control system
Note 1 to entry: A robot includes the control system and interface of a control system.
Note 2 to entry: The classification of a robot as industrial robot or service robot is done according to its intended
application.
Note 3 to entry: In order to properly perform its tasks (3.1.35), a robot makes use of different kinds of sensors to
confirm its current state and perceive the elements composing the environment in which it operates.
3.1.30
robotics
science and practice of designing, manufacturing and applying robots
[SOURCE: ISO 8373:2012, 2.16]
3.1.31
semantic computing
field of computing that aims to identify the meanings of computational content and user intentions and
to express them in a machine-processable form
3.1.32
soft computing
field of computing that is tolerant of and exploits imprecision, uncertainty and partial truth to make
problem-solving more tractable and robust
Note 1 to entry: Soft computing encompasses various techniques such as fuzzy logic, machine learning and
probabilistic reasoning.
3.1.33
symbolic AI
AI (3.1.3) based on techniques and models (3.1.23) that manipulate symbols and structures according
to explicitly defined rules to obtain inferences
Note 1 to entry: Compared to subsymbolic AI (3.1.34), symbolic AI produces declarative outputs, whereas
subsymbolic AI is based on statistical approaches and produces outputs with a given probability of error.
© ISO/IEC 2022 – All rights reserved

3.1.34
subsymbolic AI
AI (3.1.3) based on techniques and models (3.1.23) that use an implicit encoding of information, that can
be derived from experience or raw data.
Note 1 to entry: Compared to symbolic AI (3.1.33). Whereas symbolic AI produces declarative outputs,
subsymbolic AI is based on statistical approaches and produces outputs with a given probability of error.
3.1.35
task
action required to achieve a specific goal
Note 1 to entry: Actions can be physical or cognitive. For instance, computing or creation of predictions (3.1.27),
translations, synthetic data or artefacts or navigating through a physical space.
Note 2 to entry: Examples of tasks include classification, regression, ranking, clustering and dimensionality
reduction.
3.2 Terms related to data
3.2.1
data annotation
process of attaching a set of descriptive information to data without any change to that data
Note 1 to entry: The descriptive information can take the form of metadata, labels and anchors.
3.2.2
data quality checking
process in which data is examined for completeness, bias and other factors which affect its usefulness
for an AI system (3.1.4)
3.2.3
data augmentation
process of creating synthetic samples by modifying or utilizing the existing data
3.2.4
data sampling
process to select a subset of data samples intended to present patterns and trends similar to that of the
larger dataset (3.2.5) being analysed
Note 1 to entry: Ideally, the subset of data samples will be representative of the larger dataset (3.2.5).
3.2.5
dataset
collection of data with a shared format
EXAMPLE 1 Micro-blogging posts from June 2020 associated with hashtags #rugby and #football.
EXAMPLE 2 Macro photographs of flowers in 256x256 pixels.
Note 1 to entry: Datasets can be used for validating or testing an AI model (3.1.23). In a machine learning (3.3.5)
context, datasets can also be used to train a machine learning algorithm (3.3.6).
3.2.6
exploratory data analysis
EDA
initial examination of data to determine its salient characteristics and assess its quality
Note 1 to entry: EDA can include identification of missing values, outliers, representativeness for the task at hand
– see data quality checking (3.2.2).
© ISO/IEC 2022 – All rights reserved

3.2.7
ground truth
value of the target variable for a particular item of labelled input data
Note 1 to entry: The term ground truth does not imply that the labelled input data consistently corresponds to
the real-world value of the target variables.
3.2.8
imputation
procedure where missing data are replaced by estimated or modelled data
[SOURCE: ISO 20252:2019, 3.45]
3.2.9
input data
data for which an AI system (3.1.4) calculates a predicted output or inference
3.2.10
label
target variable assigned to a sample
3.2.11
personally identifiable information
PII
personal data
any information that (a) can be used to establish a link between the information and the natural person
to whom such information relates, or (b) is or can be directly or indirectly linked to a natural person
Note 1 to entry: The “natural person” in the definition is the PII principal. To determine whether a PII principal
is identifiable, account should be taken of all the means which can reasonably be used by the privacy stakeholder
holding the data, or by any other party, to establish the link between the set of PII and the natural person.
Note 2 to entry: This definition is included to define the term PII as used in this document. A public cloud PII
processor is typically not in a position to know explicitly whether information it processes falls into any specified
category unless this is made transparent by the cloud service customer.
[SOURCE: ISO/IEC 29100:2011/Amd1: 2018, 2.9]
3.2.12
production data
data acquired during the operation phase of an AI system (3.1.4), for which a deployed AI system (3.1.4)
calculates a predicted output or inference (3.1.17)
3.2.13
sample
atomic data element processed in quantities by a machine learning algorithm (3.3.6) or an AI system
(3.1.4)
3.2.14
test data
evaluation data
data used to assess the performance of a final model (3.1.23)
Note 1 to entry: Test data is disjoint from training data (3.3.16) and validation data (3.2.15).
© ISO/IEC 2022 – All rights reserved

3.2.15
validation data
development data
data used to compare the performance of different candidate models (3.1.23)
Note 1 to entry: Validation data is disjoint from test data (3.2.14) and generally also from training data (3.3.16).
However, in cases where there is insufficient data for a three-way training, validation and test set s
...