# Free ISO 22989 vocabulary check

Published contents
Contents1 Scope2 Normative references3 Terms and definitions4 Abbreviated terms5 AI conceptsClause 3, terms
3.1 Terms related to AI3.1.1 AI agent3.1.2 AI component3.1.3 artificial intelligence3.1.4 artificial intelligence system3.1.5 autonomy3.1.6 application specific integrated circuit3.1.7 automatic3.1.8 cognitive computing3.1.9 continuous learning3.1.10 connectionism3.1.11 data mining3.1.12 declarative knowledge3.1.13 expert system3.1.14 general AI3.1.15 genetic algorithm3.1.16 heteronomy3.1.17 inference3.1.18 internet of things3.1.19 IoT device3.1.20 IoT system3.1.21 knowledge3.1.22 life cycle3.1.23 model3.1.24 narrow AI3.1.25 performance3.1.26 planning3.1.27 prediction3.1.28 procedural knowledge3.1.29 robot3.1.30 robotics3.1.31 semantic computing3.1.32 soft computing3.1.33 symbolic AI3.1.34 subsymbolic AI3.1.35 task3.2 Terms related to data3.2.1 data annotation3.2.2 data quality checking3.2.3 data augmentation3.2.4 data sampling3.2.5 dataset3.2.6 exploratory data analysis3.2.7 ground truth3.2.8 imputation3.2.9 input data3.2.10 label3.2.11 personally identifiable information3.2.12 production data3.2.13 sample3.2.14 test data3.2.15 validation data3.3 Terms related to machine learning3.3.1 Bayesian network3.3.2 decision tree3.3.3 human-machine teaming3.3.4 hyperparameter3.3.5 machine learning3.3.6 machine learning algorithm3.3.7 machine learning model3.3.8 parameter3.3.9 reinforcement learning3.3.10 retraining3.3.11 semi-supervised machine learning3.3.12 supervised machine learning3.3.13 support vector machine3.3.14 trained model3.3.15 training3.3.16 training data3.3.17 unsupervised machine learning3.4 Terms related to neural networks3.4.1 activation function3.4.2 convolutional neural network3.4.3 convolution3.4.4 deep learning3.4.5 exploding gradient3.4.6 feed forward neural network3.4.7 long short-term memory3.4.8 neural network3.4.9 neuron3.4.10 recurrent neural network3.5 Terms related to trustworthiness3.5.1 accountable3.5.2 accountability3.5.3 availability3.5.4 bias3.5.5 control3.5.6 controllability3.5.7 explainability3.5.8 predictability3.5.9 reliability3.5.10 resilience3.5.11 risk3.5.12 robustness3.5.13 stakeholder3.5.14 transparency3.5.15 transparency3.5.16 trustworthiness3.5.17 verification3.5.18 validation3.6 Terms related to natural language processing3.6.1 automatic summarization3.6.2 dialogue management3.6.3 emotion recognition3.6.4 information retrieval3.6.5 machine translation3.6.6 named entity recognition3.6.7 natural language3.6.8 natural language generation3.6.9 natural language processing3.6.10 natural language processing3.6.11 natural language understanding3.6.12 optical character recognition3.6.13 part-of-speech tagging3.6.14 question answering3.6.15 relationship extraction3.6.16 sentiment analysis3.6.17 speech recognition3.6.18 speech synthesis3.7 Terms related to computer vision3.7.1 computer vision3.7.2 face recognition3.7.3 image3.7.4 image recognitionClause 5, concepts
5.1 General5.2 From strong and weak AI to general and narrow AI5.3 Agent5.4 Knowledge5.5 Cognition and cognitive computing5.6 Semantic computing5.7 Soft computing5.8 Genetic algorithms5.9 Symbolic and subsymbolic approaches for AI5.10 Data5.11 Machine learning concepts

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Nine questions, no account

# Check your AI vocabulary

Nine questions about the vocabulary your firm actually uses. The reading comes from the 117 public definitions and the amendment record, not from a score of your firm. Nothing leaves this page. A blank submit, or an answer recorded as not sure, returns scope incomplete rather than a pass.

Do you keep a controlled AI vocabulary?AnswerChoose an answerYes, with an owner and a review dateA glossary, without an ownerNoNot sureDoes your policy define AI system using a published definition?AnswerChoose an answerYes, with the citationA description in our own words onlyNo definitionNot sureIs your AI inventory keyed to those terms?AnswerChoose an answerEvery system record uses the vocabularySome records doThe inventory uses its own labelsNot sureIs there a named owner for terminology?AnswerChoose an answerYes, named and currentThe policy team, informallyNo ownerNot sureDo you map your terms to ISO/IEC 42001 clause 3?AnswerChoose an answerYes, with a mapping recordLoosely, in conversationNo mappingNot sureAre you tracking the generative AI amendment?AnswerChoose an answerYes, with a named personInformally, with no named personNoNot sureDo you keep a change log for vocabulary changes?AnswerChoose an answerYes, dated and versionedChanges are made without a recordNo change logNot sureDo procurement and model providers use the same terms?AnswerChoose an answerYes, in the contract scheduleSome suppliersNo shared vocabularyNot sureWhen is your next vocabulary review?DateThe plan works back from the earlier of this date and the planning date, 16 February 2027.

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## What the reading is, and is not

It is a reading of nine answers against the public terms and the amendment record. It names a starting point, at least three actions and a dated plan. It is not a maturity score, not an audit and not a compliance result. The product does not certify, and no answer is sent anywhere.

## Why some answers return scope incomplete

An unanswered question, or an answer recorded as not sure, returns scope incomplete and names the question. The engine does not default an unknown to the lighter pathway, because a reading built on an unknown is worse than no reading.

## What happens to the answers

They stay in this browser tab. There is no account, no server call and no analytics event carrying an answer. A completed reading adds its actions to the plan, which is kept in this browser's local storage. Nothing is sent anywhere.

Last updated: 2026-10-21 · Markdown version

Source: https://iso-22989-revision-checker.theartofservice.com/check/