DEFINITION OF AI SYSTEMS: THE COMMISSION’s GUIDELINES
This document is intended to provide a summary of the main points made in the EU Commission’s guidelines of 6 February 2025 on the definition of an AI system. The definition as is known is set out in Article 3(1) of Regulation (EU) 2024/1690 (“AI Act”). With these Guidelines, the Commission clarifies the constituent elements of the definition, with in mind also the need to identify what instead should not be considered an AI system, although characterized by certain proximity to the AI system concept under the AI Act. The full text of the Guidelines can be found here.
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Considering the wide variety of AI systems, it is not possible to provide an exhaustive list of all potential AI systems in these Guidelines
This is in line with recital 12 AI Act, which clarifies that the notion of an ‘AI system’ should be clearly defined while providing ‘the flexibility to accommodate the rapid technological developments in this field
The definition of AI system is broken down by the Commission in seven main elements:
(1) a machine-based system;
(2) that is designed to operate with varying levels of autonomy;
(3) that may exhibit adaptiveness after deployment;
(4) and that, for explicit or implicit objectives;
(5) infers, from the input it receives, how to generate outputs
(6) such as predictions, content, recommendations, or decisions
(7) that can influence physical or virtual environments
Instead, the definition acknowledges that specific elements may appear at one of the two phases (“building” and “use”) into which the AI systems’ lifecycle is broken, but may not persist across both phases.
Each of these seven elements is discussed in the Guidelines, as summarised below:
- Machine-based system
The term ‘machine’ can be understood to include both the hardware and software components that enable the AI system to function. The element of ‘machine-based’ in the definition of AI system underlines the fact that AI systems must be computationally driven and based on machine operations.
- Autonomy
The second element of the definition refers to the system being ‘designed to operate with varying levels of autonomy’. Recital 12 of the AI Act clarifies that the terms ‘varying levels of autonomy’ mean that AI systems are designed to operate with ‘some degree of independence of actions from human involvement and of capabilities to operate without human intervention’.
The reference to ‘some degree of independence of action’ in recital 12 AI Act excludes systems that are designed to operate solely with full manual human involvement and intervention. Human involvement and human intervention can be either direct, e.g. through manual controls, or indirect, e.g. though automated systems-based controls which allow humans to delegate or supervise system operations.
- Adaptiveness
Recital 12 AI Act clarifies that ‘adaptiveness’ refers to self-learning capabilities, allowing the behaviour of the system to change while in use. The new behaviour of the adapted system may produce different results from the previous system for the same inputs.
The use of the term ‘may’ in relation to this element of the definition indicates that, a system’s ability to automatically learn, discover new patterns or identify relationships in the data after deployment (i.e. beyond what it was initially trained on in the development phase) is a facultative and thus not a decisive condition for determining whether the system qualifies as an AI system.
- AI system objectives
AI systems are designed to operate according to one or more objectives. The objectives of the system may be explicitly or implicitly defined. Explicit objectives refer to clearly stated goals that are directly encoded by the developer into the system. Implicit objectives refer to goals that are not explicitly stated but may be deduced from the behaviour or underlying assumptions of the system. These objectives may arise from the training data or from the interaction of the AI system with its environment.
- Inferencing how to generate outputs using AI techniques
This capability to infer is a key, indispensable condition that distinguishes AI systems from other types of systems. As explained in recital 12, AI systems should be distinguished from “simpler traditional software systems or programming approaches and should not cover systems that are based on the rules defined solely by natural persons to automatically execute operations”.
Focusing specifically on the building phase of the AI system, recital 12 AI Act further clarifies that ‘[t]he techniques that enable inference while building an AI system include machine learning approaches that learn from data how to achieve certain objectives, and logic- and knowledge-based approaches that infer from encoded knowledge or symbolic representation of the task to be solved.’ Those techniques should be understood as ‘AI techniques’.
The first category of AI techniques mentioned in recital 12 AI Act is ‘machine learning approaches that learn from data how to achieve certain objectives’. That category includes a large variety of approaches enabling a system to ‘learn’, such as supervised learning, unsupervised learning, self-supervised learning and reinforcement learning
In addition to various machine learning approaches discussed above, the second category of techniques mentioned in recital 12 AI Act are ‘logic- and knowledge-based approaches that infer from encoded knowledge or symbolic representation of the task to be solved’. Instead of learning from data, these AI systems learn from knowledge including rules, facts and relationships encoded by human experts.
- Outputs that can influence physical or virtual environments
The capacity to generate outputs and the type of output the system can generate is central to understanding the functionality and impact of an AI system.
Outputs of AI systems belong to four broad categories listed in Article 3(1) AI Act: predictions, content, recommendations, and decisions. Each category differs in its level of human involvement
Predictions: it is an estimate about an unknown value (the output) from known values supplied to the system (the input). Software systems have been used for decades to generate predictions. AI systems using machine learning are capable of generating predictions that uncover complex patterns in data and make accurate predictions in highly dynamic and complex environments.
Content refers to the generation of new material by an AI system. This may include text, images, videos, music and other forms of output. Although content, as a category of output, may be understood from a technical perspective in terms of a sequence of ‘predictions’ or ‘decisions’, due to the prevalence of this output in generative AI systems, it is listed in recital 12 AI Act as a separate category of output
Recommendations refer to suggestions for specific actions, products, or services to users based on their preferences, behaviours, or other data inputs. In other cases, recommendations refer to potential decisions, such as a candidate to hire in a recruitment system, which will be evaluated by humans. If these recommendations are automatically applied, they become decisions.
Decisions refer to conclusions or choices made by a system. An AI system that outputs a decision automates processes that are traditionally handled by human judgement. Such a system implies a fully automated process whereby a certain outcome is produced in the environment surrounding the system without any human intervention.
- Interaction with the environment
That element should be understood to emphasise the fact that AI systems are not passive, but actively impact the environments in which they are deployed.
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CONCLUSIONS
Establishing whether a given systems qualifies as an AI system is the first, essential step to then move onto determining whether that given AI system is subject to the prohibitions or the regulatory regime for high-risk Ai systems, pursuant to respectively articles 5 and 6 of the AI Act. Other than that, all other AI systems are not subject to any regulatory requirements.
In this connection, it should be kept in mind that simpler, traditional software and programming approaches that are based on the rules defined solely by natural persons to execute operations, these fall outside of the definition of AI systems. This is so even on the event that some systems have the capacity to infer in a narrow manner, but would still fall outside of the scope of AI systems because of their limited capacity to analyse patterns and autonomously adjust their output. The Guidelnes as para. 5,2 provide examples of such excluded systems.
