Why It Is Important to Differentiate Between AI Models and AI Systems

As artificial intelligence (AI) continues to integrate into various sectors, it has become increasingly important to understand the technologies that underpin these systems. However, a common error is to confuse AI models with AI systems. While these terms are often used interchangeably, they refer to distinct components within the broader AI ecosystem. Understanding the difference between AI models and AI systems is crucial, especially given the regulatory implications under the European Union’s Artificial Intelligence Act (EU AI Act).

The EU AI Act explicitly distinguishes between AI models and AI systems, as they are regulated differently. For instance, the obligations and responsibilities placed on operators differ depending on whether they are using an AI model internally or deploying an AI system for public use. This blog post will explore the differences between AI models and AI systems, explain why this distinction matters, and outline how the EU AI Act regulates each.

Understanding AI Models

An AI model is a mathematical construct that processes input data and generates an output based on the patterns it has learned. Essentially, it is the “brain” of an AI system, capable of making predictions, classifications, or decisions based on the data it receives.

Key Components of AI Models:

1. Input Data: This is the data fed into the AI model, which could include text, images, audio, or structured data. The quality and quantity of input data are crucial for the model’s performance.
2. Algorithm: The algorithm is the set of rules or instructions the model uses to process the input data. It is through the algorithm that the model learns from data during the training phase.
3. Output Classification: The output is the result generated by the model after processing the input data. This could be a prediction, a decision, or a classification, depending on the task the model is designed to perform.

Taxonomy of AI Models:

• Foundation Models: Large-scale models that are pre-trained on a broad dataset and can be fine-tuned for specific tasks. Examples include models like GPT (Generative Pre-trained Transformer).
• Multimodal Models: Models capable of processing and integrating multiple types of data, such as text, images, and audio, to generate more comprehensive outputs.
• Large Language Models: A subtype of foundation models specifically designed for natural language processing tasks, such as language translation, text generation, and sentiment analysis.
• Diffusion Models: These models use a process of gradual refinement to generate data, often used in generative tasks like image synthesis.

Understanding AI Systems

An AI system encompasses not just the AI model but also the infrastructure and processes that enable the model to function in real-world applications. An AI system is essentially a full-fledged application that includes the model, data pipelines, user interfaces, and other components necessary for deployment and operation.

Key Components of AI Systems:

1. User Interface: The interface through which users interact with the AI system. This could be a web interface, a mobile app, or an API that connects to other systems.
2. AI Model(s): The AI model(s) within the system are responsible for processing data and generating outputs based on the algorithms they have been trained on.
3. Data Collection and Processing: This includes the mechanisms for gathering, storing, and processing data that the AI model uses. Data processing may involve cleaning, normalization, and transformation to ensure it is suitable for model input.
4. Infrastructure: The hardware and software infrastructure that supports the AI system. This could include servers, cloud platforms, databases, and other IT resources required to deploy and run the AI system at scale.

Distinguishing AI Models from AI Systems:

While AI models are a critical component of AI systems, they are just one part of a much larger framework. An AI model is the tool that performs the core task of processing data and generating outputs, but an AI system is the entire setup that includes the model as well as the interfaces, data flows, and infrastructure that make the system operational and usable.

Why the Distinction Matters: Regulatory Implications Under the EU AI Act

The EU AI Act is designed to regulate AI technologies, ensuring they are safe, transparent, and respect fundamental rights. The distinction between AI models and AI systems is crucial within this regulatory framework because the Act imposes different obligations depending on how these technologies are used and by whom.

1. Obligations for Providers of AI Models

Under the EU AI Act, providers of AI models are subject to certain obligations if they use the models for external purposes. For example, if an organization develops an AI model and licenses it to other entities for use in their AI systems, the organization is classified as a provider and must ensure the model complies with relevant regulations.

Key obligations may include:

• Transparency and Documentation: Providers must ensure that AI models are documented transparently, including details about the training data, algorithms used, and potential limitations.
• Bias Mitigation: Providers are responsible for implementing measures to detect and mitigate biases in AI models, especially if these models are used in high-risk applications.
• Monitoring and Updates: Providers must regularly monitor AI models for performance and compliance and provide updates to address any issues that arise.

2. Obligations for Providers of AI Systems

Providers of AI systems—those who deploy AI models within a broader system for public or commercial use—have a broader set of obligations under the EU AI Act. These obligations are more comprehensive because AI systems directly interact with users and are applied in real-world scenarios.

Key obligations may include:

• Risk Management: Providers must conduct risk assessments to identify and mitigate potential risks associated with the deployment of the AI system, especially if it is classified as high-risk under the EU AI Act.
• Human Oversight: AI systems must include mechanisms for human oversight, ensuring that human operators can monitor, intervene, and take responsibility for the system’s decisions.
• Transparency and Accountability: AI systems must be transparent in their operations, and providers must ensure that users understand how the system works and how decisions are made. This includes providing clear explanations and documentation.
• Compliance with Safety Standards: AI systems must comply with relevant safety standards, particularly in sectors like healthcare, finance, and autonomous vehicles, where the consequences of errors can be severe.

3. Legal Certainty and Responsibilities

The EU AI Act also emphasizes the importance of legal certainty by clearly distinguishing between AI models and AI systems. For example, an organization using an AI system for its operations is classified as a provider and is responsible for ensuring the system complies with the EU AI Act. In contrast, an organization that develops AI models but only uses them internally may not be subject to the same level of regulatory scrutiny, unless those models are used externally.

This distinction is essential for organizations to understand their responsibilities and ensure they are compliant with the appropriate regulations. By clearly defining the roles and obligations of providers of AI models and AI systems, the EU AI Act aims to create a regulatory environment that is both clear and fair, fostering innovation while protecting users and society.

Conclusion

Understanding the difference between AI models and AI systems is crucial, not only for technical clarity but also for regulatory compliance under the EU AI Act. While AI models are the core components that perform the data processing and decision-making tasks, AI systems encompass a broader framework that includes the infrastructure, user interfaces, and data processes that enable these models to function in real-world applications.

The EU AI Act differentiates between these two concepts, imposing distinct obligations on providers depending on their role in the AI ecosystem. By understanding and adhering to these distinctions, organizations can ensure they remain compliant with the EU AI Act, contribute to the responsible development and deployment of AI technologies, and avoid potential legal pitfalls.

As AI continues to evolve, the importance of clear definitions and responsibilities within the AI ecosystem will only grow. By staying informed and proactive, organizations can navigate the complex regulatory landscape and leverage AI’s potential in a way that is ethical, transparent, and aligned with societal values.