AI law glossary | AI Law Framework

AI hallucination case — A court case involving AI-fabricated citations or content; the public database had identified 2,046 worldwide by 21 Sep 2026.

AI literacy — skills, knowledge and understanding that allow providers, deployers and affected persons, taking into account their respective rights and obligations in the context of this Regulation, to make an informed deployment of AI systems, as well as to gain awareness about the opportunities and risks of AI and possible harm it can cause;

AI Office — the Commission’s function of contributing to the implementation, monitoring and supervision of AI systems and general-purpose AI models, and AI governance, provided for in Commission Decision of 24 January 2024; references in this Regulation to the AI Office shall be construed as references to the Commission;

AI regulatory sandbox — a controlled framework set up by a competent authority which offers providers or prospective providers of AI systems the possibility to develop, train, validate and test, where appropriate in real-world conditions, an innovative AI system, pursuant to a sandbox plan for a limited time under regulatory supervision;

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

AI-generated content (court use) — Content produced by a generative AI tool; court users must verify that cited cases, legislation and articles exist and support the propositions attributed to them, and be ready to identify AI-generated portions.

Artificial intelligence (AI) — a system able to perform a task that would otherwise require human intelligence.

biometric data — personal data resulting from specific technical processing relating to the physical, physiological or behavioural characteristics of a natural person, such as facial images or dactyloscopic data;

Closed AI — an AI tool not generally accessible to the public which by reason of licencing conditions or terms of service contains controls for the use of information, and protections from information being made public or being used to train the AI tool.

conformity assessment — the process of demonstrating whether the requirements set out in Chapter III, Section 2 relating to a high-risk AI system have been fulfilled;

Data Mining — The process of sorting through large data sets to identify patterns that can improve models or solve problems.

deep fake — AI-generated or manipulated image, audio or video content that resembles existing persons, objects, places, entities or events and would falsely appear to a person to be authentic or truthful;

Deep Learning — A function of AI that imitates the human brain by learning from how it structures and processes information to make decisions. Instead of relying on an algorithm that can only perform one specific task, this subset of machine learning can learn from unstructured data with supervision.

deployer — a natural or legal person, public authority, agency or other body using an AI system under its authority except where the AI system is used in the course of a personal non-professional activity;

distributor (EU AI Act) — a natural or legal person in the supply chain, other than the provider or the importer, that makes an AI system available on the Union market;

downstream provider — a provider of an AI system, including a general-purpose AI system, which integrates an AI model, regardless of whether the AI model is provided by themselves and vertically integrated or provided by another entity based on contractual relations.

emotion recognition system — an AI system for the purpose of identifying or inferring emotions or intentions of natural persons on the basis of their biometric data;

general-purpose AI model — an AI model, including where such an AI model is trained with a large amount of data using self-supervision at scale, that displays significant generality and is capable of competently performing a wide range of distinct tasks regardless of the way the model is placed on the market and that can be integrated into a variety of downstream systems or applications, except AI models that are used for research, development or prototyping activities before they are placed on the market;

general-purpose AI system — an AI system which is based on a general-purpose AI model and which has the capability to serve a variety of purposes, both for direct use as well as for integration in other AI systems;

Generative AI Chatbot or tool (GenAI Chatbot or GenAI tool) — a conversational agent that understands human language, and uses GenAI to simulate a conversation, including computer programs such as ChatGPT, Google Gemini, Claude and Microsoft Copilot.

Generative artificial intelligence (GenAI) — a subset of AI that refers to a system that enables the user to generate new content such as text, images, sound and code, in response to a question or instructions (a prompt).

Generative Pre-trained Transformer (“GPT”) — A LLM based on the transformer architecture that generates text. It is first pre-trained to predict the next token in texts. After pre-training, GPT models can generate human-like text by repeatedly predicting the token that they would expect to follow. GPT models are usually fine-tuned to reduce hallucinations or harmful behaviour, or to format the output in a conversational format.

Hallucination — An AI hallucination is when an AI system presents false information framed as the truth.

Harman undertaking — Implied undertaking not to use information produced under compulsion for any purposes extraneous to the proceedings; PIC13 lists such material among information not to be entered into Gen AI tools.

high-impact capabilities — capabilities that match or exceed the capabilities recorded in the most advanced general-purpose AI models;

importer (EU AI Act) — a natural or legal person located or established in the Union that places on the market an AI system that bears the name or trademark of a natural or legal person established in a third country;

informed consent — a subject’s freely given, specific, unambiguous and voluntary expression of his or her willingness to participate in a particular testing in real-world conditions, after having been informed of all aspects of the testing that are relevant to the subject’s decision to participate;

input data — data provided to or directly acquired by an AI system on the basis of which the system produces an output;

instructions for use — the information provided by the provider to inform the deployer of, in particular, an AI system’s intended purpose and proper use;

intended purpose — the use for which an AI system is intended by the provider, including the specific context and conditions of use, as specified in the information supplied by the provider in the instructions for use, promotional or sales materials and statements, as well as in the technical documentation;

Large Language Model (“LLM”) — LLMs are AI models which learn to predict the next best word or part of a word in a sentence having been trained on enormous quantities of text. Generative AI chatbots generally use LLMs to generate responses to “prompts”. Examples are ChatGPT and Bing Chat.

Litigation abuse (AI) — Use of processes, documents and images that is frivolous, vexatious, oppressive, harmful or misleading, or that harasses, delays or improperly burdens another court user or the Court; PD-AI says AI must not be used this way.

Machine Learning — A branch of AI that uses data and algorithms to imitate the way that humans learn, gradually improving accuracy. Through the use of statistical methods, algorithms are trained to make classifications or predictions, and to uncover key insights in data mining projects.

market surveillance authority — the national authority carrying out the activities and taking the measures pursuant to Regulation (EU) 2019/1020;

Natural Language Processing (“NLP”) — A type of AI that enables computers to understand spoken and written human language. NLP enables features like text and speech recognition on devices.

operator (EU AI Act) — a provider, product manufacturer, deployer, authorised representative, importer or distributor;

performance of an AI system — the ability of an AI system to achieve its intended purpose;

personal data — personal data as defined in Article 4, point (1), of Regulation (EU) 2016/679;

placing on the market — the first making available of an AI system or a general-purpose AI model on the Union market;

post-market monitoring system — all activities carried out by providers of AI systems to collect and review experience gained from the use of AI systems they place on the market or put into service for the purpose of identifying any need to immediately apply any necessary corrective or preventive actions;

Privacy impact assessment (PIA) — An assessment the OAIC recommends as part of a privacy-by-design approach before adopting an AI product.

profiling — profiling as defined in Article 4, point (4), of Regulation (EU) 2016/679;

Prompt — Short instructions entered to a generative AI chatbot to obtain the desired answer/output.

provider (EU AI Act) — a natural or legal person, public authority, agency or other body that develops an AI system or a general-purpose AI model or that has an AI system or a general-purpose AI model developed and places it on the market or puts the AI system into service under its own name or trademark, whether for payment or free of charge;

Public AI — an AI tool that is publicly available via the internet, operating in an open rather than closed environment, without controls as to the use or further dissemination of information. Public AI tools are trained on broad, often public datasets, most commonly for general purpose use.

putting into service — the supply of an AI system for first use directly to the deployer or for own use in the Union for its intended purpose;

reasonably foreseeable misuse — the use of an AI system in a way that is not in accordance with its intended purpose, but which may result from reasonably foreseeable human behaviour or interaction with other systems, including other AI systems;

Responsible person (submissions) — The individual named as taking responsibility for the contents of a submission; where a legal practitioner, the individual solicitor or barrister must be named, not just the firm.

Retrieval Augmented Generation (“RAG”) — RAG is a technique used in AI and natural language processing that aims to improve the quality of generated text by incorporating external information from various sources.

risk (EU AI Act) — the combination of the probability of an occurrence of harm and the severity of that harm;

serious incident — an incident or malfunctioning of an AI system that directly or indirectly leads to any of the following:

special categories of personal data — the categories of personal data referred to in Article 9(1) of Regulation (EU) 2016/679, Article 10 of Directive (EU) 2016/680 and Article 10(1) of Regulation (EU) 2018/1725;

Standing order (AI) — An order by an individual judge requiring lawyers to certify the use and accuracy of AI in filings.

substantial modification — a change to an AI system after its placing on the market or putting into service which is not foreseen or planned in the initial conformity assessment carried out by the provider and as a result of which the compliance of the AI system with the requirements set out in Chapter III, Section 2 is affected or results in a modification to the intended purpose for which the AI system has been assessed;

systemic risk — a risk that is specific to the high-impact capabilities of general-purpose AI models, having a significant impact on the Union market due to their reach, or due to actual or reasonably foreseeable negative effects on public health, safety, public security, fundamental rights, or the society as a whole, that can be propagated at scale across the value chain;

Technology Assisted Review (“TAR”) — AI tools used as part of the disclosure process to identify potentially relevant documents. In TAR a machine learning system is trained on data created by lawyers identifying relevant documents manually, then the tool uses the learned criteria to identify other similar documents from very large disclosure data sets.

testing data — data used for providing an independent evaluation of the AI system in order to confirm the expected performance of that system before its placing on the market or putting into service;

Token — In natural language processing, a token is a unit of text that is processed by the AI, typically representing a word or a part of a word. However, a token does not have a fixed length in terms of characters or words. Instead, a token can vary based on the complexity of the language and the content.

training data — data used for training an AI system through fitting its learnable parameters;

validation data — data used for providing an evaluation of the trained AI system and for tuning its non-learnable parameters and its learning process in order, inter alia, to prevent underfitting or overfitting;