EU Commission’s Guidelines on AI System Definition Under AI Act
The European Commission has published comprehensive guidelines defining what constitutes an AI system under the AI Act. This definition is pivotal as it sets the foundation for understanding how AI technologies will be regulated and assessed within the European Union.
Definition of an AI System
According to Article 3(1) of the AI Act, an AI system is characterized as a machine-based system designed to operate with varying levels of autonomy. It may exhibit adaptiveness after deployment and is tasked with inferring from inputs to generate outputs such as predictions, content, recommendations, or decisions that can influence both physical and virtual environments.
Key Elements of AI Systems
The guidelines outline seven essential elements that define an AI system:
- Machine-based system: Incorporates both hardware and software components necessary for operation.
- Varying levels of autonomy: Indicates that AI systems can function independently of human intervention to some degree.
- Adaptiveness: Refers to self-learning capabilities that enable systems to change behavior during use.
- Objectives: AI systems can have explicit or implicit goals derived from training data or environmental interactions.
- Inference capabilities: A critical feature that distinguishes AI systems, involving techniques such as machine learning.
- Output types: Outputs can be categorized into predictions, content, recommendations, and decisions.
- Environmental interaction: AI systems must have a tangible impact on their deployment environment.
Phases of AI Lifecycle
The guidelines differentiate between two main phases in the lifecycle of an AI system: pre-deployment (building) and post-deployment (use). It is important to note that not all seven elements need to be present in both phases of the AI lifecycle.
Understanding Key Concepts
1. Machine-based
The term machine-based encompasses both hardware and software components essential for AI functionality. AI systems must be computationally driven and reliant on machine operations.
2. Autonomy
Varying levels of autonomy refer to the ability of AI systems to operate independently from human involvement. This autonomy is closely tied to the system’s inference capabilities, which allow it to function without explicit human input.
3. Adaptiveness
Adaptiveness is crucial for AI systems, allowing them to self-learn and modify their behavior in response to new data. However, the guidelines clarify that a system does not necessarily need to exhibit adaptiveness after deployment to qualify as an AI system.
4. AI System Objectives
AI system objectives can be both explicit and implicit, stemming from the goals assigned to tasks or the outcomes generated through interaction with the environment.
5. Inference
The inference capacity of an AI system is considered vital for its classification as an AI system. Inference involves utilizing various techniques, including machine learning approaches like supervised learning, unsupervised learning, reinforcement learning, and more.
6. Outputs Influencing Environments
Outputs from AI systems are categorized as predictions, content, recommendations, and decisions. These outputs are distinct from non-AI systems, as they can analyze complex relationships and generate nuanced responses.
7. Interaction with the Environment
AI systems must demonstrate a tangible impact on their deployment environment, whether physical or virtual, affirming their relevance and functionality.
Exclusions from the AI Definition
The guidelines also specify certain systems that are excluded from the definition of an AI system, such as basic data processing systems and simple prediction tools that lack the capacity for autonomous pattern analysis and output adjustment.
The publication of these guidelines marks a significant step in the regulatory landscape for AI technologies in the EU, providing clarity on what constitutes an AI system and establishing a framework for compliance under the AI Act.
