Short answer
When designing or evaluating AI systems for regulatory compliance, prioritize the fundamental architecture and data processes that are difficult to change, rather than superficial user-facing elements.
- Field
- Innovation & Design
- Source
- Journal of European Public Policy (2025)
- Method
- Conceptual framework development and comparative analysis.
- Evidence
- Moderate effect
Effective regulation of AI systems requires a definition that prioritizes unchanging core characteristics over transient features, ensuring long-term applicability and enforcement. This innovation & design research insight is drawn from a 2025 study published in Journal of European Public Policy. Using Conceptual framework development and comparative analysis., researchers explored how this design variable affects real-world outcomes. The key design takeaway: When designing or evaluating AI systems for regulatory compliance, prioritize the fundamental architecture and data processes that are difficult to change, rather than superficial user-facing elements.
Defining AI Systems: Focus on Immutable Components for Robust Regulation
Effective regulation of AI systems requires a definition that prioritizes unchanging core characteristics over transient features, ensuring long-term applicability and enforcement.
Journal of European Public Policy · 2025
Key Findings
- 01The current conceptual definition of AI in the EU AI Act is insufficient for practical regulation.
- 02An operational definition should focus on immutable components of AI systems, analogous to defining an octopus by its core features rather than its changing limb appearance.
- 03Key components for regulatory focus include decision models, data, and interface design.
Application
Design takeaway
When designing or evaluating AI systems for regulatory compliance, prioritize the fundamental architecture and data processes that are difficult to change, rather than superficial user-facing elements.
How to apply
When developing new AI products or assessing existing ones for compliance, document and justify the core decision models, data pipelines, and interface designs, highlighting their stability and fundamental role.
Project actions
- 01When defining your AI system for your design project, clearly state its core components (e.g., the algorithm type, the data sources, the input/output mechanisms).
- 02Consider how these core components might be regulated and why focusing on them is important for fairness or safety.
Method & Evidence
Variables
Strengths & Limitations
Strengths
- +Provides a novel analogy (octopus) to explain a complex concept.
- +Offers a structured framework (decision models, data, interface design) for regulatory analysis.
Limitations
It can be challenging to definitively separate 'immutable' from 'mutable' aspects of a complex, evolving AI system.
Reliability & validity
The validity of the conceptual framework relies on its logical coherence and the strength of its analogy. Empirical testing would be needed to assess its practical reliability in regulatory contexts.
Think critically
If AI systems are constantly learning and adapting, how can we truly define 'immutable' components, and what are the implications for regulation?
Design Principles
"Regulate the essence, not the appearance, of AI systems."
As AI technology rapidly evolves, regulatory frameworks struggle to keep pace. By focusing on the fundamental, immutable aspects of AI development and deployment, designers and policymakers can create more resilient and adaptable guidelines that remain relevant as AI systems change.
What This Means for Your Design
To make rules for AI that actually work, we need to define AI by what it fundamentally is and how it works deep down, not by how it looks or what it's doing right now, because those things can change easily.
How to use in your project
- 1.Use the concept of immutable components to justify your design choices in the context of potential future regulations or ethical guidelines.
- 2.When discussing the 'problem definition' or 'design brief' for your project, explain why focusing on core functionalities is more robust than focusing on superficial features.
Add to My Project
Quick Cite
Paragraph starter
The operational definition of AI systems is critical for effective regulation, as highlighted by the need to focus on immutable components rather than mutable characteristics. This approach ensures that regulatory frameworks remain relevant and enforceable as AI technology evolves. By decomposing AI into core elements such as decision models, data, and interface design, designers and policymakers can establish a more robust basis for addressing potential risks and ensuring adherence to fundamental principles.
Source
Journal of European Public Policy
Pinning down an octopus: towards an operational definition of AI systems in the EU AI Act
journal · 2025
View sourceQuestions About This Research
- What does the research say about defining ai systems: focus on immutable components for robust regulation?
- When designing or evaluating AI systems for regulatory compliance, prioritize the fundamental architecture and data processes that are difficult to change, rather than superficial user-facing elements. Evidence: Journal of European Public Policy (2025).
- Why does "Defining AI Systems: Focus on Immutable Components for Robust Regulation" matter for design?
- As AI technology rapidly evolves, regulatory frameworks struggle to keep pace. By focusing on the fundamental, immutable aspects of AI development and deployment, designers and policymakers can create more resilient and adaptable guidelines that remain relevant as AI systems change.
- How can designers apply this research?
- When designing or evaluating AI systems for regulatory compliance, prioritize the fundamental architecture and data processes that are difficult to change, rather than superficial user-facing elements.
- What were the main findings?
- The current conceptual definition of AI in the EU AI Act is insufficient for practical regulation.. An operational definition should focus on immutable components of AI systems, analogous to defining an octopus by its core features rather than its changing limb appearance.. Key components for regulatory focus include decision models, data, and interface design.
- What research method was used?
- Conceptual framework development and comparative analysis..
- How strong is the evidence?
- Evidence strength is rated Moderate effect, based on a 2025 journal from Journal of European Public Policy.
- What should I do differently in my next project?
- When developing new AI products or assessing existing ones for compliance, document and justify the core decision models, data pipelines, and interface designs, highlighting their stability and fundamental role.
- What are the limitations?
- The proposed framework is conceptual and requires further empirical validation and integration into existing legal structures.