Responsible AI requires both technical safeguards and regulatory frameworks.
- Protocol design – Multi‑agent systems need deterministic messaging, provenance tags and authority delegation to prevent deadlock and corruption. The Model Context Protocol (MCP) specifies how agents share context and instruction sets; Agent‑to‑Agent (A2A) defines conversation structures for agent collaboration. Implementing these protocols improves reproducibility and auditability.
- Regulatory landscape – In 2024 the European Union finalised the AI Act, the world’s first comprehensive AI law. It categorises AI systems by risk, imposes strict requirements on high‑risk applications (e.g., medical devices, hiring), mandates transparency for general‑purpose models, and bans social‑credit‑style biometrics. Compliance deadlines begin rolling out in 2026, and Member States must establish regulatory sandboxes. Other jurisdictions—including the U.S., China, Brazil and the U.K.—are drafting their own frameworks, often focusing on privacy, discrimination and national security.
- Open vs proprietary models – Debates continue over the safety and viability of open‑source foundation models. Proponents argue that transparency fosters trust and innovation, while critics fear misuse and intellectual‑property leakage. Hybrid approaches are emerging: open‑weights models with usage licences, gated release of larger checkpoints, and enterprise‑controlled data enclaves.
This page will gather notes from panels on AI regulation, guidelines for risk assessments, and pointers to implementation resources (e.g., NIST’s AI Risk Management Framework). It will also host my commentary on balancing openness, sovereignty and global cooperation.
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