Phased AI Governance Implementation
Context
Comprehensive AI governance cannot be imposed immediately. Four constraints block rapid deployment:
- Weak public salience of systemic risks before crisis events
- Insufficient technical capacity in regulatory agencies
- Industry resistance absent competitive incentives for compliance
- Democratic legitimacy deficits when governance precedes stakeholder engagement
The Problem
Frameworks that ignore political economy fail regardless of technical merit. The challenge isn’t designing good governance, it’s building the coalitions and capacity needed to make governance stick.
The Solution: Four Phases Over Six Years
Phase 1: Foundation Building (0-24 months)
Focus: Legitimacy building through controlled pilots
- Municipal-level testing grounds for political chatbots, content moderation
- Low-risk, high-visibility settings generating empirical evidence
- Codification of constitutional principles: due process, transparency, appeals mechanisms
- Build demonstration effects through visible early successes
Phase 2: System Integration (24-48 months)
Focus: Transition from voluntary to mandatory compliance
- Mandatory risk assessments for high-impact applications (political advertising, synthetic news, voter-targeted agents)
- Operational model safety committees with enforcement authority
- Design modification powers, operational restrictions, shutdown mandates
- Standardized assessment protocols and evaluator training
Phase 3: Comprehensive Coverage (48-72 months)
Focus: Expand scope through decentralized oversight
- Extend to medium-risk scenarios via subsidiarity principle
- Local community oversight boards for localized impacts
- Capacity building for community oversight members
- Governance scales democratically rather than bureaucratically
Phase 4: Adaptive Governance (72+ months)
Focus: Institutionalize continuous learning
- Governance innovation laboratories for testing novel approaches
- Systematic threshold updates based on emerging evidence
- Regular review cycles for stakeholder representation
- Adaptive procedures for incorporating lessons from failures
Consequences
Positive:
- Coalition dynamics addressed explicitly
- First-mover advantages created for early compliance
- Technical capacity builds through learning-by-doing
- Democratic legitimacy earned rather than assumed
Negative:
- Six-year timeline may be too slow for rapidly evolving AI
- Municipal pilots may not generalize to national/global scale
- Resource-intensive capacity building requirements
- Assumes institutional willingness that may not exist
Related: 05-atom—coalition-resilience-governance, 05-atom—democratic-integrity-as-objective