EU Commission Issues Guidelines on the Definition of AI Systems
The European Commission has issued long-awaited comprehensive guidelines on the definition of AI systems, as established by Regulation (EU) 2024/1689 (AI Act). With these practical guidelines, the Commission aims to assist providers, deployers, importers, and distributors of AI systems in determining whether a system constitutes an AI system within the meaning of the AI Act, thereby facilitating the effective application and enforcement of that Act. The definition of AI systems entered into force on 2 February 2025.
Key Elements of the AI System Definition
Due to the diverse nature of AI systems, the guidelines cannot provide an exhaustive list. Each system is required to be assessed based on its specific characteristics.
The AI Act follows a lifecycle-based approach and defines an AI system as follows:
- Machine-based system
- Designed to operate with varying levels of autonomy
- May exhibit adaptiveness after deployment
- 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
This seven-point definition covers two main phases: pre-deployment (building phase) and post-deployment (use phase). Not all seven elements must be present throughout both phases; some may appear only at one stage.
Concise Summary of Each of the Seven Points
1. Machine-based System
AI systems are machine-based, which means they are developed with and run on machines and rely on both hardware and software components to function. Hardware includes processing units, memory, storage devices, and networking interfaces, while software consists of computer code, operating systems, and applications that enable data processing and task execution.
2. Autonomy
The autonomy of an AI system refers to its ability to operate with varying levels of independence from human involvement. AI systems range from those operating with full human involvement and intervention (direct or manual control) or indirect (automated oversight) to fully autonomous ones, with many falling in between. A system that processes manually provided inputs to generate an output independently still qualifies as having some degree of independence of action.
The level of autonomy is crucial for providers when determining the risk of the AI system and compliance obligations and implementing safeguards for AI deployment.
3. Adaptiveness
Adaptiveness refers to an AI system’s ability to exhibit self-learning capabilities after deployment, allowing its behaviour to change over time and produce different results for the same inputs.
4. AI System Objectives
AI systems are designed to operate based on explicit or implicit objectives that guide their functionality. Explicit objectives are clearly defined goals directly encoded by developers, while implicit objectives emerge from the system’s behaviour, training data, or interactions with its environment.
An AI system’s objectives can differ from its intended purpose, which is how it is meant to be used in a specific context.
5. Inferencing How to Generate Outputs Using AI Techniques
A key characteristic of an AI system is its ability to infer how to generate outputs from the input it receives, distinguishing it from traditional rule-based software. The inference process enables AI systems to produce predictions, content, recommendations, or decisions that influence physical and virtual environments.
There are two broad categories of inference techniques:
- Machine learning approaches that learn from data to achieve objectives, such as supervised learning, unsupervised learning, self-supervised learning, reinforcement learning, and deep learning.
- Logic- and knowledge-based approaches that infer from encoded knowledge or symbolic representations, applying rules, facts, and relationships encoded by human experts.
6. Outputs That Can Influence Physical or Virtual Environments
A key characteristic of AI systems is their ability to generate outputs that can influence physical or virtual environments. These outputs fall into four categories – predictions, content, recommendations, and decisions.
7. Interaction with the Environment
AI systems’ outputs actively impact physical and virtual environments, influencing tangible objects and digital spaces such as data flows and software ecosystems.
Systems Outside the Scope of the AI System Definition
The AI system definition does not cover systems that are based solely on rules defined by natural persons to automatically execute operations, including:
- Systems for improving mathematical optimisation that do not transcend basic data processing.
- Basic data processing systems that follow predefined, explicit instructions.
- Systems based on classical heuristics that rely on experience-based methods.
- Simple prediction systems whose performance can be achieved via basic statistical learning rules.
For advice navigating these regulations and ensuring your AI systems meet required standards, it is advisable to consult with experts in the field.
