Regulation as Learning Framework

The Principle

Complex technology regulation should be designed as an adaptive learning system, not as static rules to be enforced.

Why This Matters

Technology advances faster than legislative processes. Risks emerge that weren’t foreseeable when rules were written. Technical mitigations evolve. What counts as “reasonable” changes with state of the art. Effective governance of rapidly evolving domains requires regulation that learns and adapts, not just regulation that enforces.

The EU AI Act already embeds extensive learning mechanisms: regulatory sandboxes, post-market monitoring, delegated acts for revision, periodic legislative review. But these mechanisms are fragmented and uncoordinated. The principle demands treating them as a system.

How to Apply

Design regulatory frameworks with explicit:

• Learning inputs: What experiences and evidence will be gathered?
• Learning actors: Who participates in which learning activities?
• Learning outputs: How do learnings translate to revised guidance, standards, or rules?
• Learning coordination: How do different learning activities inform each other?

For existing regulations, map the implicit learning mechanisms and identify coordination gaps.

When This Especially Matters

• Rapidly advancing technology domains (AI, biotech, quantum)
• Areas where risks are emergent and poorly understood
• Cross-sectoral technologies that don’t fit existing regulatory categories
• Domains where fundamental rights implications are unclear

Exceptions and Boundaries

Some regulatory domains benefit from stability and predictability over adaptiveness. Contract law, property rights, basic procedural guarantees, these serve valuable functions precisely because they don’t change frequently. The principle applies most strongly where technology-driven uncertainty is high.

Related: 05-molecule—regulatory-learning-space-framework, 05-atom—horizontal-vertical-regulation-tension, 05-atom—fundamental-rights-direct-effect-problem