1

EU-U.S. Terminology and Taxonomy for Artificial Intelligence 

First Edition

Introduction

The European Union (EU) and the United States (U.S.) are committed to cooperating on technologies and a digital 

transformation based on shared democratic values. The Trade and Technology Council (TTC) provides a platform 

for EU and U.S. policymakers and stakeholders to shape the future of transatlantic cooperation on Artificial 

Intelligence (AI). 

As policy frameworks on AI emerge both in the EU and in the U.S., as well as in many other like-minded countries 

worldwide, the importance of aligning terminology and conceptual frameworks is becoming increasingly evident. 

Converging, interoperable approaches to defining and framing AI risks and trustworthiness are essential to enhance 

legal certainty, promote effective risk management, speed up the identification of emerging risks and reduce 

compliance costs and administrative burdens. This, in turn, is expected to foster innovation, maximising the benefits 

of AI systems and at the same time managing its risks. Ultimately the alignment of terminologies will help foster the 

EU-U.S. joint leadership in the development of an international standard for Trustworthy AI based on a mutual respect 

for human rights and democratic values.

As stated in the EU-U.S. Third Ministerial Statement, the first Joint Roadmap on Evaluation and Measurement Tools 

for Trustworthy AI and Risk Management (AI Roadmap) serves to inform the approaches to AI risk management and 

Trustworthy AI on both sides of the Atlantic, and advance collaborative approaches in international standards bodies 

related to AI. Following the Roadmap suggestions for concrete activities aimed at aligning EU and U.S. risk-based 

approaches, a group of experts engaged to prepare an initial draft AI terminologies and taxonomies. A total number 

of 65 terms were identified with reference to key documents from the EU and the U.S. (see methodology below for 

more information).

The identified terms reflect a shared technical, socio-technical and values-based understanding of AI systems between 

the EU and U.S. and will serve as a foundation for future definitions, as well as future transatlantic cooperation on AI 

terminology and taxonomy. This list should be considered as preliminary, to be further expanded and validated also 

with input from experts and stakeholders in the coming months. 

Why AI Terminology Matters

AI terminology is pivotal to cooperation on AI in part due to the present momentum in the field, and due to the broader 

role of language in constructing and explaining scientific paradigms. Terminology is a necessary basis for technical 

standards and creates shared frames of reference between like-minded partners and across disciplines. Ultimately, 

different terminologies express distinct “technological cultures,” thus revealing, through both alignment and 

divergence, the existence of gaps, unnecessary divergences and inconsistencies, and other points of departure for 

cooperation and collaboration. 

The EU and U.S. understanding is based on the term “Trustworthy AI.” According to the EU HLEG Trustworthy AI 

has three components: (1) it should be lawful, ensuring compliance with all applicable laws and regulations (2) it 

should be ethical, demonstrating respect for, and ensure adherence to, ethical principles and values and (3) it should 

be robust, both from a technical and social perspective, since, even with good intentions, AI systems can cause 

unintentional harm. According to the NIST AI Risk Management Framework (AI RMF), characteristics of trustworthy 

AI systems include: valid and reliable, safe, secure and resilient, accountable and transparent, explainable and 

interpretable, privacy enhanced, and fair with their harmful biases managed. Trustworthy AI concerns not only the 

trustworthiness of the AI system itself but also comprises the trustworthiness of all processes and actors that are part 

of the AI system’s life cycle. 

In this context, different approaches to the development and governance of AI systems are currently competing at a 

regional and global level, resulting in distinct visions of technological systems based on the cultures of scientists and 

entrepreneurs as well as requirements and expectations from users, adopters, developers and lawmakers. The EU and 
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U.S. agree on the pursuit of a human-centric approach to AI: this requires that the terminology adopted to implement 

our shared approach to AI centres human, societal and environmental well-being, as well as the rule of law, human 

rights, democratic values and sustainable development. 

Limitations and purpose of the terminology presented in this document

The list of terms presented in this document does not aim at achieving complete harmonisation or total alignment 

between the two legal systems. The EU and U.S. both recognise and respect their individual regulatory, social and 

cultural contexts, which in some instances may necessitate different definitions.

Furthermore, the list presented below does not include terms that are currently being discussed and defined in 

legislative processes in the EU and/or U.S., in order not to interfere with these. 

Stakeholder Engagement 

This document represents the first edition of the EU-U.S. Terminology and Taxonomy for Artificial Intelligence 

developed by the Working Group members according to the criteria and methodology presented below. This edition 

will be presented to AI experts and a broad community of stakeholders in the EU and the U.S. to receive feedback and 

contributions towards its enhancement and expansion. We therefore warmly encourage all stakeholders to share 

comments with the Working Group. Mechanisms for communication will be announced after the Fourth TTC 

Ministerial Meeting. These will be detailed separately. 

Methodology

This list was built by the Working Group 1 experts from the EU and the U.S. in three steps. They initially defined a 

broad framework by agreeing on key criteria for selecting terms, largely based on existing official documents at the 

national and international level, as well as international standards documents and research publications. The selected 

terms were categorised into different clusters, and finally a list of terms are presented in this document. Below, these 

steps are described in more detail.

It must be noted that although many of the terms in this list can apply to several emerging technologies and 

technological systems, the terms in this list are only considered in the specific context of AI socio-technical systems.

1. Initial Step

a. The primary selection criteria were the following: 

i. Is this term essential to understanding a risk-based approach to AI?

ii. Does the definition of this term serve to advance EU-U.S. cooperation on AI? 

b. In defining terms, the experts turned to existing definitions found in widely-recognized documents such as 

academic literature, institutional references and the key EU-U.S. policy documents listed in the TTC Joint 

Roadmap for Trustworthy AI and Risk Management; and when needed tailored them to the context of AI. 

2. Refined Step

Building upon the initial reference framework, the EU and U.S. experts further refined the selection of terms by 

undertaking the following exercises: 

c. Jointly categorising terms as

● Foundational: those terms which are essential to understanding the risk-based approach to the AI, and 

are relevant to and defined by both the EU and the U.S. 

● Pending: those terms whose definition is fixed or not changeable at this time due to legislative or other 

institutional processes occurring in either the EU or the U.S. These terms may be revisited in future 

revisions and efforts under the broader umbrella of the Joint AI Roadmap Implementation. 
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d. The EU and U.S. experts then compared and examined existing definitions and framing documents to find terms 

of greatest coherence or alignment between the EU and the U.S. 

3. Proposed List of Terms 

e. Through the process outlined above, the Working Group 1 experts have identified a preliminary list of terms 

which are believed to be essential to developing a transatlantic understanding of the risk-based approach to AI. 

f. These terms reflect the shared understandings of AI systems between the EU and U.S. and may serve as a 

foundation for the ongoing work of the Working Group 1 and future transatlantic cooperation on AI terminology 

and taxonomy. 

g. Annex A lists pending key terms that are currently involved in legislative or other institutional processes, and 

thus were excluded from the WG 1 efforts at this juncture. 
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List of Terms: 

Note: the references in this table are identified by a shorthand ID which is reflected in the references table in Annex B. 

1. Cluster: AI Lifecycle

Term EU definition Reference U.S. definition Reference Final definition Final Source

adversarial machine 

learning (adversarial 

attack)

An input to a Machine 

Learning (ML) model 

that is purposely 

designed to cause a 

model to make a mistake 

in its predictions despite 

resembling a valid input 

to a human.

JRC A practice concerned with the 

design of ML algorithms that 

can resist security challenges, 

the study of the capabilities of 

attackers, and the 

understanding of attack 

consequences.

Reznik,_Leon A practice concerned with the 

design of ML algorithms that 

can resist security challenges, 

the study of the capabilities of 

attackers, and the 

understanding of attack 

consequences. Inputs in 

adversarial ML are purposely 

designed to make a mistake in 

its predictions despite 

resembling a valid input to a 

human.

Combination based on 

JRC and Reznik,_Leon

autonomy 

(autonomous AI 

system)

JRC The system has a set of 

intelligence-based capabilities 

that allows it to respond to 

situations that were not preprogrammed or anticipated 

(i.e., decision-based 

responses) prior to system 

deployment. Autonomous 

systems have a degree of selfgovernment and self-directed 

behavior (with the human’s 

proxy for decisions). 

DOD_TEVV Systems that maintain a set of 

intelligence-based capabilities 

to respond to situations that 

were not pre-programmed or 

anticipated (i.e., decision-based 

responses) prior to system 

deployment. Autonomous 

systems have a degree of selfgovernment and self-directed 

behaviour (with the human’s 

proxy for decisions).

DOD_TEVV

big data An all-encompassing 

term for any collection 

of data sets so large or 

complex that they are 

difficult to store, 

manage and process 

with conventional, nonscalable technology.

JRC Extremely large data sets that 

are statistically analyzed to 

gain detailed insights. The 

data can involve billions of 

records and require 

substantial computerprocessing power. Datasets 

are sometimes linked together 

to see how patterns in one 

domain affect other areas. 

Brookings_Inst

itution

An all-encompassing term for 

large, complex digital data sets 

that need equally complex 

technological means to be 

stored, analysed, managed and 

processed with substantial 

computing power. Datasets 

are sometimes linked together 

to see how patterns in one 

domain affect other areas. Data 

Combination based on 

JRC and 

Brookings_Institution
5

Term EU definition Reference U.S. definition Reference Final definition Final Source

Data can be structured into 

fixed fields or unstructured as 

free-flowing information. The 

analysis of big datasets, often 

using AI, can reveal patterns, 

trends, or underlying 

relationships that were not 

previously apparent to 

researchers.

can be structured into fixed 

fields or unstructured as freeflowing information. The 

analysis of big datasets, often 

using AI, can reveal patterns, 

trends, or underlying 

relationships that were not 

previously apparent to 

researchers.

classifier A model that predicts (or 

assigns) class labels to data 

input.

own definition 

based on 

expertise

A model that predicts (or 

assigns) class labels to data 

input.

Own definition based 

on expertise.

data poisoning Data poisoning occurs 

when an adversarial 

actor attacks an AI 

system training set, thus 

making the AI system 

learn something that it 

should not learn. 

Examples show that in 

some cases these data 

poisoning attacks on 

neural nets can be very 

effective, causing a 

significant drop in 

accuracy even with very 

little data poisoning. 

Other kinds of poisoning 

attacks do not aim to 

change the behaviour of 

the AI system, but rather 

they insert leverage to 

get the AI system to do 

what they want.

EU 

HLEG/ALT

AI

Machine learning systems 

trained on user-provided data 

are susceptible to data 

poisoning attacks, whereby 

malicious users inject false 

training data with the aim of 

corrupting the learned model

Steinhardt,_Jac

ob

A type of security attack where 

malicious users inject false 

training data with the aim of 

corrupting the learned model, 

thus making the AI system 

learn something that it should 

not learn.

Combination based on 

HLEG/ALTAI and 

Steinhardt,_Jacob
6

Term EU definition Reference U.S. definition Reference Final definition Final Source

deep learning Deep learning is part of 

a broader family of 

machine learning 

methods based on 

artificial neural 

networks with 

representation learning. 

Deep learning 

architectures have been 

applied to fields 

including computer 

vision, speech 

recognition, natural 

language processing, 

machine translation, 

bioinformatics, drug 

design, medical image 

analysis, climate 

science, material 

inspection and board 

game programs, where 

they have produced 

results comparable to 

and in some cases 

surpassing human expert 

performance.

DL_1

DL_2

A subset of machine learning 

that relies on neural networks 

with many layers of neurons. 

In so doing, deep learning

employs statistics to spot 

underlying trends or data 

patterns and applies that 

knowledge to other layers of 

analysis. Some have labeled 

this as a way to “learn by 

example” and a technique that 

“perform[s] classification 

tasks directly from images, 

text, or sound” and then 

applies that knowledge 

independently.Deep learning 

requires extensive computing 

power and labeled data, and is 

used in medical research, 

automated vehicles, 

electronics, and 

manufacturing, among other 

areas.

Brookings_Inst

itution

A subset of machine learning 

based on artificial neural 

networks that employs 

statistics to spot underlying 

trends or data patterns and 

applies that knowledge to other 

layers of analysis. Some have 

labelled this as a way to “learn 

by example” and as a technique 

that “perform[s] classification 

tasks directly from images, 

text, or sound” and then applies 

that knowledge independently.

Combination based on 

DL_1, DL_2 and 

Brookings_Institution

differential privacy Differential privacy is a 

meaningful and 

mathematically rigorous 

definition of privacy 

useful for quantifying 

and bounding privacy 

loss. Developed in the 

context of statistical 

disclosure control –

providing accurate 

statistical information 

Dwork_ECS Differential privacy is a 

method for measuring how 

much information the output 

of a computation reveals 

about an individual. It is 

based on the randomised 

injection of "noise". Noise is 

a random alteration of data in 

a dataset so that values such 

as direct or indirect identifiers 

of individuals are harder to 

reveal. An important aspect 

of differential privacy is the 

privacyenhancing_tec

hnologies

Differential privacy is a 

method for measuring how 

much information the output of 

a computation reveals about an 

individual. It produces data 

analysis outcomes that are 

nearly equally likely, whether 

any individual is, or is not, 

included in the dataset. Its goal 

is to obscure the presence or 

absence of any individual (in a 

database), or small groups of 

individuals, while at the same 

Combination based on 

privacyenhancing_technologie

s and Dwork_ECS
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Term EU definition Reference U.S. definition Reference Final definition Final Source

about a set of 

respondents while 

protecting the privacy of 

each individual – the 

concept applies more 

generally to any private 

data set for which it is 

desirable to release 

coarse-grained 

information while 

keeping private the 

details. Informally, 

differential privacy 

requires the probability 

distribution on the 

published results of an 

analysis to be 

“essentially the same,” 

independent of whether 

any individual opts in to 

or opts out of the data 

set. The probabilities are 

over the coin flips of the 

data analysis algorithm.

concept of “epsilon” or ɛ, 

which determines the level of 

added noise. Epsilon is also 

known as the “privacy 

budget” or “privacy 

parameter”.

time preserving statistical 

utility.

input data Data provided to or 

directly acquired by an 

AI system on the basis 

of which the system 

produces an output.

EU AIA IEEE_Soft_Vo

cab

Data provided to or directly 

acquired by an AI system on 

the basis of which the system 

produces an output.

Combination based on 

EU AIA and 

IEEE_Soft_Vocab

machine learning Machine Learning (ML) 

is a branch of artificial 

intelligence (AI) and 

computer science which 

focuses on development 

JRC A general approach for 

determining models from 

data.

AI_Fairness_3

60

Machine Learning is a branch 

of artificial intelligence (AI) 

and computer science which 

focuses on development of 

systems

Combination based on 

JRC and 

AI_Fairness_360.
8

Term EU definition Reference U.S. definition Reference Final definition Final Source

of systems that are able 

To learn and adapt 

without following 

explicit instructions 

imitating the way that 

humans learn, gradually 

improving its accuracy, 

by using algorithms and 

statistical models to 

analyse and draw 

inferences from patterns 

in data.

that are able to learn and adapt 

Without following explicit 

instructions imitating the way 

that humans learn, gradually 

improving its accuracy, by 

using algorithms and statistical 

models to analyse and draw 

inferences from patterns in 

data.

model training Process to establish or to 

improve the parameters 

of a machine learning 

model, based on a 

Machine Learning 

algorithm,by using 

training data.

ISO/IEC DIS 

22989 

Machine 

Learning

The phase in the data science 

development lifecycle where 

practitioners try to fit the best 

combination of weights and 

bias to a machine learning 

algorithm to minimize a loss 

function over the prediction 

range.

C3.ai_Model_

Training

Process to establish or to 

improve the parameters of a 

machine learning model, based 

on a Machine Learning 

algorithm, by using training 

data.

ISO/IEC DIS22989 

Machine Learning

model validation Confirmation through 

the provision of 

objective evidence, that 

the requirements for a 

specific intended use or 

application have been 

fulfilled.

ISO/IEC 

DIS22989

The set of processes and 

activities intended to verify 

that models are performing as 

expected.

yields.io_mode

l_validation

Confirmation through the 

provision of objective 

evidence, that the requirements 

for a specific intended use or 

application have been fulfilled.

ISO/IEC DIS22989

natural language 

processing

Information processing 

based upon natural 

language understanding 

and natural language 

generation.

Discipline concerned 

with the way computers 

process natural language 

data.

ISO/IEC in 

JRC

A computer's attempt to 

“understand” spoken or 

written language. It must 

parse vocabulary, grammar, 

and intent, and allow for 

variation in language use. The 

process often involves 

machine learning.

Hutson,_Matth

ew

The ability of a machine to 

process, analyse, and mimic 

human language, either spoken 

or written.

Own definition based 

on ISO/IEC in JRC and 

Hutson_Matthew
9

Term EU definition Reference U.S. definition Reference Final definition Final Source

predictive analysis Predictive analytics: this 

forward-looking 

technique aims to 

support the business in 

predicting what could 

happen by analysing 

backward-looking data. 

This involves the use of 

advanced data-mining 

and statistical techniques 

such as ML. The goal is 

to improve the accuracy 

of predicting a future 

event by analysing 

backward-looking data.

European 

Banking 

Authority

The organization of analyses 

of structured and unstructured 

data for inference and 

correlation that provides a 

useful predictive capability to 

new circumstances or data.

IEEE_Guide_I

PA

The organisation of analyses of 

structured and unstructured 

data for inference and 

correlation that provides a 

useful predictive capability to 

new circumstances or data.

IEEE_Guide_IPA

profiling ‘Profiling’ means any 

form of automated 

processing of personal 

data consisting of the 

use of personal data to 

evaluate certain personal 

aspects relating to a 

natural person, in 

particular to analyse or 

predict aspects 

concerning that natural 

person's performance at 

work, economic 

situation, health, 

personal preferences, 

interests, reliability, 

behaviour, location or 

movements.

GDPR ‘Profiling’ means any form of 

automated processing of 

personal data consisting of 

the use of personal data to 

evaluate certain personal 

aspects relating to a natural 

person, in particular to 

analyse or predict aspects 

concerning that natural 

person's performance at work, 

economic situation, health, 

personal preferences, 

interests, reliability, 

behaviour, location or 

movements.

GDPR ‘Profiling’ means any form of 

automated processing of 

personal data consisting of the 

use of personal data to evaluate 

certain personal aspects 

relating to a natural person, in 

particular to analyse or predict 

aspects concerning that natural 

person's performance at work, 

economic situation, health, 

personal preferences, interests, 

reliability, behaviour, location 

or movements.

GDPR

reinforcement 

learning

Machine Learning 

utilizing a reward 

function to optimize 

either a policy function 

or a value function by 

sequential interaction 

ISO/IEC in 

JRC

A type of machine learning in 

which the algorithm learns by 

acting toward an abstract 

goal, such as “earn a high 

video game score” or 

“manage a factory 

Hutson,_Matth

ew

A type of machine learning in 

which the algorithm learns by 

acting toward an abstract goal, 

such as “earn a high video 

game score” or “manage a 

factory efficiently.” During 

Combination based on 

Hutson,_Matthew and 

ISO/IEC in JRC
10

Term EU definition Reference U.S. definition Reference Final definition Final Source

with an environment. 

Note 1 to entry: Policy 

functions and value 

functions express a 

strategy that is learned 

by the environment. 

Note 2 to entry: The 

environment can be any 

stateful model.

efficiently.” During training, 

each effort is evaluated based 

on its contribution toward the 

goal.

training, each effort is 

evaluated based on its 

contribution toward the goal. 

structured data Data that has a predefined 

data model or is organized in 

a predefined way.

NIST_1500 Data that has a predefined data 

model or is organised in a 

predefined way.

NIST_1500

unstructured data Data that does not have a 

predefined data model or is 

not organized in a predefined 

way.

Own definition 

based on 

NIST_1500

Data that does not have a 

predefined data model or is not 

organised in a predefined way.

Own definition based 

on NIST_1500

synthetic data Synthetic data is 

artificial data that is 

generated from original 

data and a model that is 

trained to reproduce the 

characteristics and 

structure of the original 

data. This means that 

synthetic data and 

original data should 

deliver very similar 

results when undergoing 

the same statistical 

analysis. The degree to 

which synthetic data is 

an accurate proxy for the 

original data is a 

measure of the utility of 

the method and the 

model. The generation 

process, also called 

synthesis, can be 

performed using 

EDPS_SD Synthetic data can mean 

many different things 

depending upon the way they 

are used. Sometimes, as in 

computer programming, the 

term means data that are 

completely simulated for 

testing purposes. Other times, 

as in statistics, the term 

means combining data, often 

from multiple sources, to 

produce estimates for more 

granular populations than any 

one source can support. An 

example of this usage is the 

U.S. Census Bureau’s Small 

Area Income and Poverty 

Estimates. In data 

confidentiality applications, 

synthetic data are modeled 

statistical outputs released in 

a format that closely 

resembles the confidential 

U.S. Census Synthetic data is generated 

from data/processes and a 

model that is trained to 

reproduce the characteristics 

and structure of the original 

data aiming for similar 

distribution. 

The degree to which synthetic 

data is an accurate proxy for 

the original data is a measure 

of the utility of the method and 

the model. 

Own definition based 

on EDPS_SD
11

Term EU definition Reference U.S. definition Reference Final definition Final Source

different techniques, 

such as decision trees, or 

deep learning 

algorithms. Synthetic 

data can be classified 

with respect to the type 

of the original data: the 

first type employs real 

datasets, the second 

employs knowledge 

gathered by the analysts 

instead, and the third 

type is a combination of 

these two. Generative 

Adversarial Networks 

(GANs) were introduced 

recently and are 

commonly used in the 

field of image 

recognition. They are 

generally composed of 

two neural networks 

training each other 

iteratively. The 

generator network 

produces synthetic 

images that the 

discriminator network 

tries to identify as such 

in comparison to real 

images.

data format. Synthetic data 

can be disaggregated to the 

individual- or business-record 

level, or aggregated into 

tabular format.

transfer learning A technique in machine 

learning in which an 

algorithm learns to perform 

one task, such as recognizing 

cars, and builds on that 

knowledge when learning a 

Hutson,_Matth

ew

A technique in machine 

learning in which an algorithm 

learns to perform one task, 

such as recognizing cars, and 

builds on that knowledge when 

learning a different but related 

task, such as recognizing cats.

Hutson,_Matthew
12

Term EU definition Reference U.S. definition Reference Final definition Final Source

different but related task, 

such as recognizing cats.

supervised learning Machine learning that 

makes use of labelled 

data during training

ISO/IEC 

DIS22989

Machine learning that makes 

use of labelled data during 

training.

ISO/IEC DIS22989

unsupervised learning Machine learning that 

makes use of unlabelled 

data during training.

ISO/IEC in 

JRC

Algorithms, which take a set 

of data consisting only of 

inputs and then they attempt 

to cluster the data objects 

based on the similarities or 

dissimilarities in them.

Reznik,_Leon Machine learning that makes 

use of unlabelled data during 

training.

ISO/IEC in JRC
13

2. Cluster: Measurement

Term EU definition Reference U.S. definition Reference Final definition Final Source

(AI) accuracy The goal of an AI model 

is to learn patterns that 

generalize well for 

unseen data. It is 

important to check if a 

trained AI model is 

performing well on 

unseen examples that 

have not been used for 

training the model. To 

do this, the model is used 

to predict the answer on 

the test dataset and then 

the predicted target is 

compared to the actual 

answer. The concept of 

accuracy is used to 

evaluate the predictive 

capability of the AI 

model. Informally, 

accuracy is the fraction 

of predictions the model 

got right. A number of 

metrics are used in 

machine learning (ML) 

to measure the predictive 

accuracy of a model. The 

choice of the accuracy 

metric to be used 

depends on the ML task.

EU 

HLEG/ALT

AI

Closeness of computations 

or estimates to the exact or 

true values that the statistics 

were intended to measure.

OECD 

Closeness of computations or 

estimates to the exact or true 

values that the statistics were 

intended to measure. The goal 

of an AI model is to learn 

patterns that generalise well for 

unseen data. It is important to 

check if a trained AI model is 

performing well on unseen 

examples that have not been 

used for training the model. To 

do this, the model is used to 

predict the answer on the test 

dataset and then the predicted 

target is compared to the actual 

answer. The concept of 

accuracy is used to evaluate the 

predictive capability of the AI 

model. Informally, accuracy is 

the fraction of predictions the 

model got right. A number of 

metrics are used in machine 

learning (ML) to measure the 

predictive accuracy of a model. 

The choice of the accuracy 

metric to be used depends on 

the ML task.

Combination based on 

EU HLEG/ALTAI and 

OECD. 

Test Technical operation to 

determine one or more 

characteristics of or to 

evaluate the performance of 

a given product, material, 

equipment, organism, 

NSCAI Technical operation to 

determine one or more 

characteristics of or to evaluate 

the performance of a given 

product, material, equipment, 

organism, physical 

NSCAI
14

Term EU definition Reference U.S. definition Reference Final definition Final Source

physical phenomenon, 

process or service according 

to a specified procedure. OR

Activity in which a system 

or component is executed 

under specified conditions, 

the results are observed or 

recorded, and an evaluation 

is made of some aspect of 

the system or component 

phenomenon, process or 

service according to a specified 

procedure.

Evaluation Systematic determination of 

the extent to which an entity 

meets its specified criteria

NSCAI Systematic determination of 

the extent to which an entity 

meets its specified criteria.

NSCAI

Verification Provides evidence that the 

system or system element 

performs its intended 

functions and meets all 

performance requirements 

listed in the system 

performance specification

NSCAI Provides evidence that the 

system or system element 

performs its intended functions 

and meets all performance 

requirements listed in the 

system performance 

specification.

NSCAI

Validation Confirmation by 

examination and provision 

of objective evidence that 

the particular requirements 

for a specific intended use 

are fulfilled

NSCAI Confirmation by examination 

and provision of objective 

evidence that the particular 

requirements for a specific 

intended use are fulfilled.

NSCAI

Test and Evaluation, 

Verification and 

Validation (TEVV) 

A framework for assessing, 

incorporating methods and 

metrics to determine that a 

technology or system 

satisfactorily meets its 

design specifications and 

requirements, and that it is 

sufficient for its intended 

use.

NSCAI A framework for assessing, 

incorporating methods and 

metrics to determine that a 

technology or system 

satisfactorily meets its design 

specifications and 

requirements, and that it is 

sufficient for its intended use.

NSCAI
15

3. Cluster: Technical System Attributes

Term EU definition Reference U.S. definition Reference Final definition Final Source

adaptive learning An adaptive AI is a 

system that changes its 

behaviour while in use. 

Adaptation may entail a 

change in the weights of 

the model or a change in 

the internal structure of 

the model itself. The new 

behaviour of the adapted 

system may produce 

different results than the 

previous system for the 

same inputs.

ISO/IEC DIS 

22989 

Trustworthin

ess

Updating predictive models 

online during their operation 

to react to concept drifts

Gama_Joao An adaptive AI is a system that 

changes its behaviour while in 

use. Adaptation may entail a 

change in the weights of the 

model or a change in the 

internal structure of the model 

itself. The new behaviour of 

the adapted system may 

produce different results than 

the previous system for the 

same inputs.

ISO/IEC DIS 22989 

Trustworthiness

algorithm An algorithm consists of 

a set of instructions or 

steps used to solve a 

problem (e.g., it does not 

include the data). The 

algorithm can be abstract 

and implemented in 

different programming 

languages and software 

libraries.

JRC A set of step-by-step 

instructions. Computer 

algorithms can be simple (if 

it's 3 p.m., send a reminder) 

or complex (identify 

pedestrians).

Huston,_Matthe

w

An algorithm consists of a set 

of step-by-step instructions to 

solve a problem (e.g., not 

including data). The algorithm 

can be abstract and 

implemented in different 

programming languages and 

software libraries.

Combination based on 

JRC and 

Huston_Matthew

classification When the output is one of a 

finite set of values (such as 

sunny, cloudy or rainy), the 

learning problem is called 

classification, and is called 

Boolean or binary 

classification if there are 

only two values.

AIMA A classification system is a set 

of “boxes” into which things 

are sorted. Classifications are 

consistent, have unique 

classificatory principles, and 

are mutually exclusive. In AI 

design, when the output is one 

of a finite set of values (such as 

sunny, cloudy or rainy), the 

learning problem is called 

classification, and is called 

Boolean or binary 

Own definition based 

on AIMA and 

definition of 

classification by 

Bowker and Star. 
16

Term EU definition Reference U.S. definition Reference Final definition Final Source

classification if there are only 

two values.

federated learning Federated learning is a 

relatively new way of 

developing machinelearning models where 

each federated device 

shares its local model 

parameters instead of 

sharing the whole dataset 

used to train it. The 

federated learning 

topology defines the way 

parameters are shared. In 

a centralised topology, 

the parties send their 

model parameters to a 

central server that uses 

them to train a central 

model which in turn 

sends back updated 

parameters to the parties. 

In other topologies, such 

as the peer-to-peer or 

hierarchical one, the 

parties share their 

parameters with a subset 

of their peers.

Federated learning is a 

potential solution for 

developing machinelearning models that 

require huge or very 

disperse datasets. 

However, it is not a onesize-fits-all machine 

learning scenarios

EDPS_FL a learning model which 

addresses the problem of 

data governance and privacy 

by training algorithms 

collaboratively without 

transferring the data to 

another location.

Public_Health_a

nd_Informatics_

MIE_2021 

Federated learning is a machine 

learning model which 

addresses the problem of data 

governance and privacy by 

training algorithms 

collaboratively without 

transferring the data to another 

location. Each federated device 

shares its local model 

parameters instead of sharing 

the whole dataset used to train 

it and the federated learning 

topology defines the way 

parameters are shared.

Own definition based 

on combination of 

EDPS_FL and 

Public_Health_and_In

formatics_MIE_2021 
17

Term EU definition Reference U.S. definition Reference Final definition Final Source

generative 

adversarial network 

(GAN)

Generative Adversarial 

Networks, or GANs for 

short, are an approach to 

generative modeling using 

deep learning methods, such 

as convolutional neural 

networks. Generative 

modeling is an unsupervised 

learning task in machine 

learning that involves 

automatically discovering 

and learning the regularities 

or patterns in input data in 

such a way that the model 

can be used to generate or 

output new examples that 

plausibly could have been 

drawn from the original 

dataset.

Brownlee,_Jason

_GAN

Generative Adversarial 

Networks, or GANs for short, 

are an approach to generative 

modelling using deep learning 

methods, such as convolutional 

neural networks. Generative 

modelling is an unsupervised 

learning task in machine 

learning that involves 

automatically discovering and 

learning the regularities or 

patterns in input data in such a 

way that the model can be used 

to generate or output new 

examples that plausibly could 

have been drawn from the 

original dataset.

Brownlee,_Jason_GA

N
18

Term EU definition Reference U.S. definition Reference Final definition Final Source

human values for AI "Respect for rule of law, 

human rights and 

democratic values"

The European Union 

declares the fundamental 

EU values to be the ones 

"common to the Member 

States in a society in 

which pluralism, nondiscrimination, tolerance, 

justice, solidarity and 

equality between women 

and men prevail". They 

are: human dignity, 

freedom, democracy, 

equality, rule of law, and 

human rights.

EP Human 

Rights Fact 

Sheet

Artificial intelligence 

systems use data we generate 

in our daily lives and as such 

are a mirror of our interests, 

weaknesses, and differences. 

Artificial intelligence, like 

any other technology, is not 

value-neutral. Understanding 

the values behind the 

technology and deciding on 

how we want our values to 

be incorporated in AI 

systems requires that we are 

also able to decide on how 

and what we want AI to 

mean in our societies. It 

implies deciding on ethical 

guidelines, governance 

policies, incentives, and 

regulations. And it also 

implies that we are aware of 

differences in interests and 

aims behind AI systems 

developed by others 

according to other cultures 

and principles. *See note.

Virginia_Dignu

m_Responsibilit

y_and_Artificial

_Intelligence

Values are idealised qualities 

or conditions in the world that 

people find good.

AI systems are not valueneutral. The incorporation of 

human values into AI systems 

requires that we identify 

whether, how and what we 

want AI to mean in our 

societies. It implies deciding on

ethical principles, governance 

policies, incentives, and 

regulations. And it also implies 

that we are aware of 

differences in interests and 

aims behind AI systems 

developed by others according 

to other cultures and principles.

The EU and U.S. are 

committed to the development 

of Trustworthy AI systems 

based on shared democratic 

values including the respect for 

the rule of law and human 

rights. 

Own definition based 

on EU and U.S. values 

and Brey

human-centric AI

AI is not an end in itself, 

but a tool that has to 

serve people with the 

ultimate aim of increasing 

human well-being.

HLEG AI, 

Ethics 

Guidelines 

for 

Trustworthy 

AI. 

An approach to AI that 

prioritises human ethical 

responsibility, dynamic 

qualities, understanding and 

meaning. It encourages the 

Own definition based 

on Hasselbalch, G. 

(2021) Data Ethics of 

Power - A Human 
19

Term EU definition Reference U.S. definition Reference Final definition Final Source

The human-centric 

approach to AI strives to 

ensure that human values 

are central to the way in 

which AI systems are 

developed, deployed, 

used and monitored, by 

ensuring respect for 

fundamental rights, 

including those set out in 

the Treaties of the 

European Union and 

Charter of Fundamental 

Rights of the European 

Union, all of which are 

united by reference to a 

common foundation 

rooted in respect for 

human dignity, in which 

the human being enjoy a 

unique and inalienable 

moral status. This also 

entails consideration of 

the natural environment 

and of other living beings 

that are part of the human 

ecosystem, as well as a 

sustainable approach 

enabling the flourishing 

of future generations to 

come.

empowerment of humans in 

design, use and implementation 

of AI systems.

Human-Centric AI systems are 

built on the recognition of a 

meaningful human-technology 

interaction. They are designed 

as components of sociotechnical environments in 

which humans assume 

meaningful agency. 

Human-Centric AI is not 

designed as an end in itself, but 

as tools to serve people with 

the ultimate aim of increasing 

human and environmental 

well-being with respect for the 

rule of law, human rights, 

democratic values and 

sustainable development.

Approach to Big Data 

and AI, Edward Elgar; 

HLEG Ethics

Guidelines for 

Trustworthy AI. 

language model A language model is an 

approximative description that 

captures patterns and 

regularities present in natural 

language and is used for making 

assumptions on previously 

unseen language fragments.

Gustavii,_Ebba A language model is an 

approximative description that 

captures patterns and regularities 

present in natural language and is 

used for making assumptions on 

previously unseen language 

fragments.

Gustavii,_Ebba 
20

Term EU definition Reference U.S. definition Reference Final definition Final Source

large language 

model (LLM)

A class of language models 

that use deep-learning 

algorithms and are trained on 

extremely large textual 

datasets that can be multiple 

terabytes in size. LLMs can 

be classed into two types: 

generative or discriminatory. 

Generative LLMs are models 

that output text, such as the 

answer to a question or even 

writing an essay on a specific 

topic. They are typically 

unsupervised or semisupervised learning models 

that predict what the 

response is for a given task. 

Discriminatory LLMs are 

supervised learning models 

that usually focus on 

classifying text, such as 

determining whether a text 

was made by a human or AI.

AI_Assurance_2

022 

A class of language models 

that use deep-learning 

algorithms and are trained on 

extremely large textual datasets 

that can be multiple terabytes 

in size. LLMs can be classed 

into two types: generative or 

discriminatory. Generative 

LLMs are models that output 

text, such as the answer to a 

question or even writing an 

essay on a specific topic. They 

are typically unsupervised or 

semi-supervised learning 

models that predict what the 

response is for a given task. 

Discriminatory LLMs are 

supervised learning models that 

usually focus on classifying 

text, such as determining 

whether a text was made by a 

human or AI

AI_Assurance_2022 

model The workflow of an AI 

model shows the phases 

needed to build the model 

and their 

interdependencies. 

Typical phases are: Data 

collection and 

preparation, Model 

development, Model 

training, Model accuracy 

evaluation, 

Hyperparameters’ tuning, 

Model usage, Model 

maintenance, Model 

versioning. These stages 

are usually iterative: one 

EU 

HLEG/ALT

AI

A function that takes features 

as input and predicts labels 

as output.

AI_Fairness_360 A function that takes features 

as input and predicts labels as 

output. Typical phases of an AI 

model’s work flow are: Data 

collection and preparation, 

Model development, Model 

training, Model accuracy 

evaluation, Hyperparameters’ 

tuning, Model usage, Model 

maintenance, Model 

versioning.

Combination based on 

EU HLEG/ALTAI 

and AI_Fairness_360
21

Term EU definition Reference U.S. definition Reference Final definition Final Source

may need to reevaluate 

and go back to a previous 

step at any point in the 

process.

neural network Network of two or more 

layers of neurons 

connected by weighted 

links with adjustable 

weights, which takes 

input data and produces 

an output. Note 1 to 

entry: Whereas some 

neural networks are 

intended to simulate the 

functioning of biological 

neurons in the nervous 

system, most neural 

networks are used in 

artificial intelligence as 

realizations of the 

connectionist model.

ISO/IEC in

JRC

Also known as artificial 

neural network, neural net, 

deep neural net; a computer 

system inspired by living 

brains.

Ranschaert,_Erik A computer system inspired by 

living brains, also known as 

artificial neural network, neural 

net, or deep neural net. It 

consists of two or more layers 

of neurons connected by 

weighted links with adjustable 

weights, which takes input data 

and produces an output. 

Whereas some neural networks 

are intended to simulate the 

functioning of biological 

neurons in the nervous system, 

most neural networks are used 

in artificial intelligence as 

realisations of the connectionist 

model.

Own definition based 

on ISO/IEC in JRC 

and Ranschaert,_Erik

scalability The ability to increase or 

decrease the computational 

resources required to execute 

a varying volume of tasks, 

processes, or services.

IEEE_Guide_IP

A

The ability to increase or 

decrease the computational 

resources required to execute a 

varying volume of tasks, 

processes, or services.

IEEE_Guide_IPA

socio-technical 

system

Technology is always 

part of society, just like 

society is always part of 

technology.

This also means that one 

cannot understand one 

without the other.

Technology is not only 

design and material 

appearance but also 

sociotechnical; that is, a 

complex process 

(Hasselbach 

(2021) based 

on Hughes, 

1987, 1983; 

Bijker et al., 

1987; Misa, 

1988, 1992, 

2009; Bijker 

& Law, 

1992; 

Edwards, 

2002; 

how humans interact with 

technology within the 

broader societal context.

Technology is always part of 

society, just like society is 

always part of technology. This 

also means that one cannot 

understand one without the 

other. Technology is not only 

design and material appearance 

but also sociotechnical; that is, 

a complex process constituted 

by diverse social, political, 

economic, cultural and 

technological factors.

Hasselbalch (2021), 

based on Hughes, 

1987, 1983; Bijker et 

al., 1987; Misa, 1988, 

1992, 2009; Bijker & 

Law, 1992; Edwards, 

2002; Harvey et al., 

2017 etc.
22

Term EU definition Reference U.S. definition Reference Final definition Final Source

constituted by diverse 

social, political, 

economic, cultural and 

technological factors.

Harvey et al., 

2017 etc.).

technical 

interoperability

The ability of software or 

hardware systems or 

components to operate 

together successfully with 

minimal effort by end user.

SP1011 The ability of software or 

hardware systems or 

components to operate together 

successfully with minimal 

effort by an end user.

SP1011

value sensitive 

design (values-bydesign or ethics-bydesign)

A theoretically grounded 

approach to the design of 

technology that accounts for 

human values in a principled 

and systematic manner 

throughout the design 

process.

Friedman_et_al_

2017

A theoretically grounded 

approach to the design of 

technology that accounts for 

human values in a principled 

and systematic manner 

throughout the design process.

Friedman_et_al_2017
23

4. Cluster: Governance

Term EU definition Source U.S. definition Source Final definition Final Source

auditability of an AI 

system

Auditability refers to the 

ability of an AI system to 

undergo the assessment of 

the system’s algorithms, 

data and design processes. 

This does not necessarily 

imply that information 

about business models and 

Intellectual Property 

related to the AI system 

must always be openly 

available. Ensuring 

traceability and logging 

mechanisms from the early 

design phase of the AI 

system can help enable the 

system's auditability. 

EU 

HLEG/A

LTAI

Systematic, independent, 

documented process for 

obtaining records, statements of 

fact, or other relevant 

information and assessing them 

objectively, to determine the 

extent to which specified 

requirements are fulfilled.

IEEE_Soft_Voc

ab

Auditability refers to the ability 

of an AI system to undergo the 

assessment of the system’s 

algorithms, data and design 

processes. This does not 

necessarily imply that 

information about business 

models and Intellectual Property 

related to the AI system must 

always be openly available. 

Ensuring traceability and 

logging mechanisms from the 

early design phase of the AI 

system can help enable the 

system's auditability. 

EU HLEG/ALTAI

standard "Standards are a set of 

institutionalised agreed uponrules for the production

of (textual or material) 

objects. They are released by 

international organizations 

and ensure quality and safety 

and set product or services’ 

specifications. Standards are 

the result of negotiations 

among various stakeholders 

and are institutionalised and 

thus difficult to change."

loosely 

based 

Bowker

and Star

Standards are a set of 

institutionalised agreed upon-rules 

for the production

of (textual or material) objects. 

They are released by international 

organisations and ensure quality and 

safety and set product or services’ 

specifications. Standards are the 

result of negotiations among various 

stakeholders and are 

institutionalised and thus difficult to 

change.

Loosely based on 

Bowker and Star
24

5. Cluster: Trustworthy

Term EU definition Reference U.S. definition Reference Final definition Final Source

accessibility Extent to which products, 

systems, services, 

environments and 

facilities can be used by 

people from a population 

with the widest range of 

user needs, characteristics 

and capabilities to achieve 

identified goals in 

identified contexts of use 

(which includes direct use 

or use supported by 

assistive technologies).

EU 

HLEG/AL

TAI

Extent to which products, 

systems, services, environments 

and facilities can be used by 

people from a population with 

the widest range of user needs, 

characteristics and capabilities to 

achieve identified goals in 

identified contexts of use (which 

includes direct use or use 

supported by assistive 

technologies).

EU HLEG/ALTAI

accountability This term refers to the 

idea that one is 

responsible for their 

action – and as a corollary 

their consequences – and 

must be able to explain 

their aims, motivations, 

and reasons. 

Accountability has several 

dimensions. 

Accountability is 

sometimes required by 

law. For example, the 

General Data Protection 

Regulation (GDPR) 

requires organisations that 

process personal data to 

ensure security measures 

are in place to prevent 

data breaches and report if 

these fail. But 

accountability might also 

express an ethical 

standard, and fall short of 

EU 

HLEG/AL

TAI

Accountability relates to an 

allocated responsibility. The 

responsibility can be based on 

regulation or agreement or 

through assignment as part of 

delegation; 2) For systems, a 

property that ensures that 

actions of an entity can be 

traced uniquely to the entity; 3) 

In a governance context, the 

obligation of an individual or 

organization to account for its 

activities, for completion of a 

deliverable or task, accept the 

responsibility for those 

activities, deliverables or tasks, 

and to disclose the results in a 

transparent manner.

ISO/IEC_TS_5

723:2022

Accountability relates to an 

allocated responsibility. The 

responsibility can be based on 

regulation or agreement or

through assignment as part of 

delegation. In a systems context, 

accountability refers to systems 

and/or actions that can be traced 

uniquely to a given entity. In a 

governance context, 

accountability refers to the 

obligation of an individual or 

organisation to account for its 

activities, to complete a 

deliverable or task, to accept the 

responsibility for those 

activities, deliverables or tasks, 

and to disclose the results in a 

transparent manner.

Combination based 

on EU 

HLEG/ALTAI and 

ISO/IEC_TS_5723:2

022
25

Term EU definition Reference U.S. definition Reference Final definition Final Source

legal consequences. Some 

tech firms that do not 

invest in facial 

recognition technology in 

spite of the absence of a 

ban or technological 

moratorium might do so 

out of ethical 

accountability 

considerations.

AI (or algorithmic) 

bias

AI (or algorithmic) bias 

describes systematic and 

repeatable errors in a 

computer system that 

create unfair outcomes, 

such as favouring one 

arbitrary group of users 

over others. Bias can 

emerge due to many 

factors, including but not 

limited to the design of 

the algorithm or the 

unintended or 

unanticipated use or 

decisions relating to the 

way data is coded, 

collected, selected or used 

to train the algorithm. 

Bias can enter into 

algorithmic systems as a 

result of pre-existing 

cultural, social, or 

institutional expectations; 

because of technical 

limitations of their design; 

or by being used in 

unanticipated contexts or 

by audiences who are not 

considered in the 

EU 

HLEG/AL

TAI

A systematic error. In the 

context of fairness, we are 

concerned with unwanted bias 

that places privileged groups at 

systematic advantage and 

unprivileged groups at 

systematic disadvantage.

AI_Fairness_36

0

Harmful AI bias describes 

systematic and repeatable errors 

in AI systems that create unfair 

outcomes, such as placing. 

privileged groups at systematic 

advantage and unprivileged 

groups at systematic 

disadvantage. Different types 

of bias can emerge and interact 

due to many factors, including 

but not limited to, human or 

system decisions and processes 

across the AI lifecycle. Bias can 

be present in AI systems 

resulting from pre-existing 

cultural, social, or institutional 

expectations; because of 

technical limitations of their 

design; by being used in 

unanticipated contexts; or by 

non-representative design 

specifications. 

Combination based 

on EU 

HLEG/ALTAI and 

AI_Fairness_360
26

Term EU definition Reference U.S. definition Reference Final definition Final Source

software's initial design. 

AI bias is found across 

platforms, including but 

not limited to search 

engine results and social 

media platforms, and can 

have impacts ranging 

from inadvertent privacy 

violations to reinforcing 

social biases of race, 

gender, sexuality, and 

ethnicity.

attack Model inversion refers to 

a kind of attack to AI 

models, in which the 

access to a model is 

abused to infer 

information about the 

training data. So, model 

inversion turns the usual 

path from training data 

into a machine-learned 

model from a one-way 

one to a two-way one, 

permitting the training 

data to be estimated from 

the model with varying 

degrees of accuracy. Such 

attacks raise serious 

concerns given that 

training data usually 

contain privacy-sensitive 

information.

EU 

HLEG/AL

TAI

Action targeting a learning 

system to cause malfunction. 

NISTIR_8269_

Draft

Action targeting a learning 

system to cause malfunction. 

NISTIR_8269_Draft

chatbot 

(conversational bot)

A computer program 

designed to simulate 

conversation with a 

human user, usually over 

the internet; especially 

one used

Oxford 

English 

Dictionary 

Conversational agent that

dialogues with its user (for 

example: empathic robots 

available to patients, or 

automated conversation 

services in customer relations).

COE_AI_Gloss

ary

A computer program designed to 

simulate conversation with a 

human user, usually over the 

internet; especially one used

Oxford English 

Dictionary
27

Term EU definition Reference U.S. definition Reference Final definition Final Source

to provide information or 

assistance to the user as 

part of an automated 

service.

to provide information or 

assistance to the user as part of 

an automated service.

discrimination Pre-existing bias comes 

from the outside of the 

computer system. It can 

be individual or social, 

and it already exists in 

social contexts and in the 

personal biases and 

attitudes held by the 

developers of the system. 

This type of bias is 

embedded in a computer 

system either explicitly 

and deliberately or 

implicitly and 

undeliberately by 

institutions or individuals. 

Technical bias comes 

from technical constraints 

or limitations, like 

imperfections in 

pseudorandom number 

generation that, for 

example, systematically 

favour those at the end of 

a database. 

Finally, emergent bias 

appears in the context of 

use of a computer 

system." 

The aim of nondiscrimination law is to 

EU LEX Disadvantageous treatment of a 

person based on belonging to a 

category rather than on 

individual merit.

Žliobaitė_Indrė Unequal treatment of a person 

based on belonging to a category 

rather than on individual merit. 

Discrimination can be a result of 

societal, institutional and 

implicitly held individual biases 

or attitudes that get captured in 

processes across the AI lifecycle, 

including by AI actors and 

organisations, or represented in 

the data underlying AI systems. 

Discrimination biases can also 

emerge due to technical 

limitations in hardware or 

software, or the use of an AI 

system that, due to its context of 

application, does not treat all 

groups equally. Discriminatory 

biases can also emerge in the 

very context in which the AI 

system is used. As many forms 

of biases are systemic and 

implicit, they are not easily 

controlled or mitigated and 

require specific governance and

other similar approaches"

Own definition 

loosely based on 

Friedman, B. and 

Nissenbaum, H.; and 

Schwartz, R. et al. 
28

Term EU definition Reference U.S. definition Reference Final definition Final Source

allow all individuals an 

equal and fair chance to 

access opportunities 

available in a society. 

This means that 

individuals or groups of 

individuals which are in 

comparable situations 

should not be treated less 

favourably simply 

because of a particular 

characteristic such as their 

sex, racial or ethnic 

origin, religion or belief, 

disability, age or sexual 

orientation.

evasion Evasion is one of the most 

common attacks on 

machine learning models 

(ML) performed during 

production. It refers to 

designing an input, which 

seems normal for a human 

but is wrongly classified 

by ML models. A typical 

example is to change 

some pixels in a picture 

before uploading, so that 

the image recognition 

system fails to classify the 

result.

EU 

HLEG/AL

TAI

In Evasion Attacks, the 

adversary solves a constrained 

optimization problem to find a 

small input perturbation that 

causes a large change in the 

loss function and results in 

output misclassification. 

tabassi_adversa

rial_2019

In Evasion Attacks, the 

adversary solves a constrained 

optimization problem to find a 

small input perturbation that 

causes a large change in the loss 

function and results in output 

misclassification. 

tabassi_adversarial_

2019

fault tolerance Fault tolerance is the 

property that enables a 

system to continue 

operating properly in the 

event of the failure of (or 

one or more faults within) 

some of its components. 

EU 

HLEG/AL

TAI

The ability of a system or 

component to continue normal 

operation despite the presence 

of hardware or software faults

SP1011 The ability of a system or 

component to continue normal 

operation despite the presence of 

hardware or software faults.

SP1011
29

Term EU definition Reference U.S. definition Reference Final definition Final Source

If its operating quality 

decreases at all, the 

decrease is proportional to 

the severity of the failure, 

as compared to a naively 

designed system, in which 

even a small failure can 

cause total breakdown. 

Fault tolerance is 

particularly sought after 

in high-availability or 

safety- critical systems. 

Redundancy or 

duplication is the 

provision of additional 

functional capabilities that 

would be unnecessary in a 

fault-free environment. 

This can consist of 

backup components that 

automatically ‘kick in’ if 

one component fails.

feedback loop describes the process of 

leveraging the output of an AI 

system and corresponding enduser actions in order to retrain 

and improve models over time. 

The AI-generated output 

(predictions or 

recommendations) are 

compared against the final 

decision (for example, to 

perform work or not) and 

provides feedback to the 

model, allowing it to learn 

from its mistakes.

C3.ai_feedback

_loop

Feedback loop describes the 

process of leveraging the output 

of an AI system and 

corresponding end-user actions 

in order to retrain and improve 

models over time. The AIgenerated output (predictions or 

recommendations) are compared 

against the final decision (for 

example, to perform work or 

not) and provides feedback to 

the model, allowing it to learn 

based on its results.

Own definition 

based on 

C3.ai_feedback_loo

p
30

Term EU definition Reference U.S. definition Reference Final definition Final Source

harmful bias Harmful bias can be either 

conscious or unconscious. 

Unconscious, also known as 

implicit bias, involves 

associations outside conscious 

awareness that lead to a 

negative evaluation of a person 

on the basis of characteristics 

such as race, gender, sexual 

orientation, or physical 

ability.3,14 Discrimination is 

behavior; discriminatory 

actions perpetrated by 

individuals or institutions refer 

to inequitable treatment of 

members of certain social 

groups that results in social 

advantages or disadvantages

humphrey_addr

essing_2020

Harmful bias can be either 

conscious or unconscious. 

Unconscious, also known as 

implicit bias, involves 

associations outside conscious 

awareness that lead to a negative 

evaluation of a person on the 

basis of characteristics such as 

race, gender, sexual orientation, 

or physical ability. 

Discrimination is behaviour; 

discriminatory actions 

perpetrated by individuals or 

institutions refer to inequitable 

treatment of members of certain 

social groups that results in 

social advantages or 

disadvantages.

AI systems can reinforce 

harmful bias when trained on 

prejudiced or unrepresentative 

data. Most often harmful bias is 

unintended by developers and 

adopters of AI. AI actors can 

design AI systems to mitigate 

harmful bias. 

humphrey_addressin

g_2020

human rights 

impact assessment

The rights people are 

entitled to simply because 

they are human beings, 

irrespective of their 

citizenship, nationality, 

race, ethnicity, language, 

gender, sexuality, or 

abilities; human rights 

become enforceable when 

they are codified as 

conventions, covenants, 

or treaties.

DIHR Impact assessment definition -

a risk management tool that 

seeks to ensure an organization 

has sufficiently considered a 

system's relative benefits and 

costs before implementation. In 

the context of AI, an impact 

assessment helps to answer a 

simple question: alongside this 

system’s intended use, for 

whom could it fail?

An human rights impact 

assessment (HRIA) of AI 

identifies, understands and 

assesses the impact of the AI 

system on human rights, such as 

but not limited to, the right to 

privacy or non-discrimination. 

AI systems can pose risks to, as 

well as enhance, individual 

human rights. 

Own definition 

based on DIHR 
31

Term EU definition Reference U.S. definition Reference Final definition Final Source

opacity The opacity refers to the 

lack of transparency on 

the process by which AI 

system reaches a result. 

An AI system can be 

transparent (or conversely 

opaque) in three different 

ways: with respect to how 

exactly the AI system 

functions as a whole

(functional transparency); 

how the algorithm was 

realized in code 

(structural transparency) 

and how the program 

actually run in a particular 

case, including the 

hardware and input data 

(run transparency).

Algorithms often no 

longer take the form of 

more or less easily 

readable code, but instead 

resemble a ‘black-box’. 

This means that while it 

maybe be possible to test 

the algorithm as to its 

effects, but not to 

understand how those 

effects have been 

achieved. 

Some AI systems lack 

transparency because the 

rules followed, which lead 

from input to output, are 

not fully prescribed by a 

human. Rather, is some 

cases, the algorithm is set 

to learn from data in order 

EU AIA

(Impact 

Assessment 

of the AI 

Act, Annex 

5.2)

[to receive] the output of [an] 

algorithm (the classification 

decision) [and to not] have any 

concrete sense of how or why a 

particular classification has 

been arrived at from inputs.

When AI system processes, 

functions, output or behavior 

are unavailable or 

incomprehensible to all 

stakeholders - usually an 

antonym for transparency.

Jenna_Burrell When AI system processes, 

functions, output or behaviour 

are unavailable or 

incomprehensible to all 

stakeholders – usually an 

antonym for transparency.

Jenna_Burrell
32

Term EU definition Reference U.S. definition Reference Final definition Final Source

to arrive at a pre-defined 

output in the most 

efficient way, which 

might not be 

representable by rules 

which a human could

understand. As a result, 

AI systems are often 

opaque in a way other 

digital systems are not 

(‘the so called black box 

effect’). Independently 

from technical 

characteristics, a lack of 

transparency can also 

stem from systems relying 

on rules

and functionalities that 

are not publicly accessible 

and of which a 

meaningful and accurate 

description is not publicly 

accessible. The 

complexity and lack of 

transparency (opacity of 

AI) makes it difficult to 

identify and prove 

possible breaches of laws, 

including legal provisions 

that protect fundamental 

rights.

red-team Red teaming is the 

practice whereby a red 

team or independent 

group challenges an 

organisation to improve 

its effectiveness by 

assuming an adversarial 

EU 

HLEG/AL

TAI

A group of people authorized 

and organized to emulate a 

potential adversary’s attack or 

exploitation capabilities against 

an enterprise’s security posture. 

The Red Team’s objective is to 

improve enterprise 

CSRC A group of people authorised 

and organised to emulate a 

potential adversary’s attack or 

exploitation capabilities against 

an enterprise’s security posture. 

It is often used to help identify 

Combination based 

on EU 

HLEG/ALTAI and 

CSRC
33

Term EU definition Reference U.S. definition Reference Final definition Final Source

role or point of view. It is 

often used to help identify 

and address potential 

security vulnerabilities.

cybersecurity by demonstrating 

the impacts of successful 

attacks and by demonstrating 

what works for the defenders 

(i.e., the Blue Team) in an 

operational environment. Also 

known as Cyber Red Team.

and address potential security 

vulnerabilities.

reliability An AI system is said to be 

reliable if it behaves as 

expected, even for novel 

inputs on which it has not 

been trained or tested 

earlier.

EU 

HLEG/AL

TAI

Reliability refers to the 

closeness of the initial 

estimated value(s) to the 

subsequent estimated values.

OECD An AI system is said to be 

reliable if it behaves as expected, 

even for novel inputs on which it 

has not been trained or tested 

earlier.

EU HLEG/ALTAI

resilience The ability to prepare for and 

adapt to changing conditions 

and withstand and recover 

rapidly from disruptions. 

Resilience includes the ability 

to withstand and recover from 

deliberate attacks, accidents, or 

naturally occurring threats or 

incidents. The ability of a 

system to adapt to and recover 

from adverse conditions.

NISTIR_8269_

Draft

The ability of an AI system to 

prepare for and adapt to 

changing conditions and 

withstand and recover rapidly 

from disruptions. Resilience 

includes the ability to withstand 

and recover from deliberate 

attacks, accidents, or naturally 

occurring threats or incidents. 

The ability of a system to adapt 

to and recover from adverse 

conditions.

NISTIR_8269_Draft

robustness (robust 

AI) 

Robustness of an AI 

system encompasses both 

its technical robustness 

(appropriate in a given 

context, such as the 

application domain or life 

cycle phase) and as well 

as its robustness from a 

social perspective 

(ensuring that the AI 

system duly takes into 

account the context and 

environment in which the 

system operates). This is 

EU 

HLEG/AL

TAI

ability of a system to maintain 

its level of performance under 

a variety of circumstances

ISO/IEC_TS_5

723:2022

Robustness of an AI system 

encompasses both its technical 

robustness (ability of a system to 

maintain its level of performance 

under a variety of 

circumstances) as well as its 

robustness from a social 

perspective (ensuring that the AI 

system duly takes into account 

the context and environment in 

which the system operates). This 

is crucial to ensure that, even 

with good intentions, no 

unintentional harm can occur. 

Own definition 

based EU 

HLEG/ALTAI and 

ISO/IEC 

TS_5723:2022
34

Term EU definition Reference U.S. definition Reference Final definition Final Source

crucial to ensure that, 

even with good intentions, 

no unintentional harm can 

occur. Robustness is the 

third of the three 

components necessary for 

achieving Trustworthy 

AI.

safety AI safety is described as 

mitigating accident risks 

from machine learning 

systems. “The problem of 

accidents in machine 

learning systems. We 

define accidents as 

unintended and harmful 

behaviour that may 

emerge from machine 

learning systems

when we specify the 

wrong objective function, 

are not careful about the 

learning process, or 

commit other machine 

learning related 

implementation errors.”

Amodei AI systems should not, under 

defined conditions, lead to a 

state in which human life, 

health, property, or the 

environment is endangered.

ISO ISO/IEC 

TS 5723:2022

AI systems should not, under 

defined conditions, lead to a 

state in which human life, health, 

property, or the environment is 

endangered.

ISO ISO/IEC TS 

5723:2022

security The protection mechanisms, 

design and maintenance of an 

AI system and infrastructure’s 

AI systems that can maintain 

confidentiality, integrity, and

availability through protection 

mechanisms.

NIST_AI_RMF

_1.0

The protection mechanisms, 

design and maintenance of an AI 

system and infrastructure’s AI 

systems that can maintain 

confidentiality, integrity, and

availability through protection 

mechanisms.

NIST_AI_RMF_1.0

systemic bias Systemic biases result from 

procedures and practices of 

particular institutions that 

operate in ways which result in 

certain social groups being 

D. Chandler 

and R. Munday

Systemic bias is a social 

consistent structure of harmful 

bias that is systemically 

reinforced in institutions, 

cultural perception and socioOwn definition 

loosely based on D. 

Chandler and R. 

Munday
35

Term EU definition Reference U.S. definition Reference Final definition Final Source

advantaged or favored and 

others being disadvantaged or 

devalued. This need not be the 

result of any conscious 

prejudice or discrimination but 

rather of the majority following 

existing rules or norms.

technical infrastructures. AI 

systems can reinforce systemic 

biases by reproducing the 

discriminatory effects of

systemic biases when deployed 

in socially important institutions, 

cultural production or in societal 

infrastructures. 

traceability Ability to track the 

journey of a data input 

through all stages of 

sampling, labelling, 

processing and decision 

making.

EU 

HLEG/AL

TAI

Ability to trace the history, 

application or location of an 

entity by means of recorded 

identification. ["Chain of 

custody" is a related term.] 

Alternatively, traceability is a 

property of the result of a 

measurement or the value of a 

standard whereby it can be 

related with a stated 

uncertainty, to stated 

references, usually national or 

international standards, i.e. 

through an unbroken chain of 

comparisons. In this context, 

the standards referred to here 

are measurement standards 

rather than written standards.

UNODC_Gloss

ary_QA_GLP

Ability to track the journey of a 

data input through all stages of 

sampling, labelling, processing 

and decision making.

EU HLEG/ALTAI

Trustworthy AI Trustworthy AI has three 

components: (1) it should 

be lawful, ensuring 

compliance with all 

applicable laws and 

regulations (2) it should 

be ethical, demonstrating 

respect for, and ensure 

adherence to, ethical 

principles and values and 

(3) it should be robust, 

both from a technical and 

social perspective, since, 

EU 

HLEG/AL

TAI

Characteristics of Trustworthy 

AI systems include: valid and 

reliable, safe, secure and 

resilient, accountable and 

transparent, explainable and 

interpretable, privacyenhanced, and fair with 

harmful bias managed.

NIST_AI_RMF

_1.0

Trustworthy AI has three 

components: (1) it should be 

lawful, ensuring compliance 

with all applicable laws and 

regulations (2) it should be 

ethical, demonstrating respect 

for, and ensure adherence to, 

ethical principles and values and 

(3) it should be robust, both from 

a technical and social 

perspective, since, even with 

good intentions, AI systems can 

cause unintentional harm. 

Own definition 

based on 

combination of EU 

HLEG/ALTAI and 

NIST_AI_RMF_1.0
36

Term EU definition Reference U.S. definition Reference Final definition Final Source

even with good intentions, 

AI systems can cause 

unintentional harm. 

Trustworthy AI concerns 

not only the 

trustworthiness of the AI 

system itself but also 

comprises the 

trustworthiness of all 

processes and actors that 

are part of the AI 

system’s life cycle.

Characteristics of Trustworthy 

AI systems include: valid and 

reliable, safe, secure and 

resilient, accountable and 

transparent, explainable and 

interpretable, privacy-enhanced, 

and fair with harmful bias 

managed. Trustworthy AI 

concerns not only the 

trustworthiness of the AI system 

itself but also comprises the 

trustworthiness of all processes 

and actors that are part of the AI 

system’s life cycle. 
37

Annex A. Pending Terms: 

1. Artificial general intelligence (AGI)

2. Artificial intelligence (AI)

3. Biometric categorisation system

4. Biometric data

5. Conformity

6. Data governance

7. Data quality

8. Deepfake

9. Deployment

10. Developer

11. Distributor

12. Documentation

13. Emotion recognition system

14. Foundation models

15. Fundamental rights impact assessment

16. Generative artificial intelligence

17. Harm

18. Importer

19. Incident

20. Instructions for use (usability)

21. Life cycle of an AI system

22. Minimisation

23. Misuse

24. Model risk management

25. Operator

26. Provider

27. Pseudo-anonymization (pseudonymization)

28. Recall of an AI system

29. Rectification

30. Remote biometric identification system

31. Risk

32. Risk control

33. Sandbox

34. Sensitive data

35. Subliminal techniques
38

36. Substantial modification

37. Testing data

38. Testing in real world conditions

39. Training data

40. User

41. Validation
39 

Annex B. References

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I 

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European 

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on the free 

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43 

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intelligence — 

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concepts and 

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ISO https://www.iso.

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46 

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deciding+on+ho

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8