AI-governance meta-debates · 5 catalogued
What the AI-governance field is unsettled on
Five field-level disputes where regulator practice, academic consensus, and frontier-lab policy currently diverge. Each debate carries named positions, their proponents, and the primary sources behind each position — a structured-controversy schema, not a tertiary "neutral" summary.
open-vs-closed-frontier
Open-Source vs Closed-Source Frontier Models
Should the most-capable AI models be released under permissive licenses (open weights), or only via API / structured-access agreements? The dispute is foundational to nearly every frontier-AI governance instrument.
pause-vs-accelerate
Pause AI vs Accelerate Capabilities
Should the global community impose temporary or capability-conditional pauses on frontier-AI development, or should development accelerate with safety work conducted in parallel?
pre-vs-post-deployment-eval
Pre-Deployment Red-Team vs Post-Deployment Audit
Should AI capability + safety evaluations happen primarily before deployment (red-team gating release), or primarily after (post-deployment audit + incident response)?
risk-vs-principles-vs-liability
Risk-Based vs Principles-Based vs Ex-Post Liability Regimes
Should AI governance work via (a) risk-based ex-ante categorisation + obligations (EU), (b) high-level principles delegated to sector regulators (UK / OECD / G7), or (c) ex-post liability + civil litigation (US sectoral)?
compute-vs-behavioral-threshold
Compute vs Behavioural Capability Thresholds
Should the regulatory trigger for 'frontier' / 'foundation' / 'systemic-risk' status be training-compute thresholds (objective + ex-ante observable), or behavioural-capability evaluation (more semantically meaningful but operationally costly)?