AI's Accountability Gap: A Policy Blueprint for Policymakers

Abstract
AI systems now influence medical decisions, legal filings, financial advice, education, and crisis interactions for over 100 million weekly users—yet no jurisdiction requires vendors to measure or disclose how reliably these systems function during extended use. Models marketed with “million-token” context windows degrade predictably at 10-30% of advertised capacity, but users receive no warnings. Failures remain invisible until harm occurs.
This policy blueprint addresses AI’s accountability gap through three enforceable pillars: (1) Adverse event registries enabling systematic harm detection, (2) Verification infrastructure through AI Conversational Phenomenology (ACP) and Evans’ Law providing measurable reliability limits and mandatory disclosure, and (3) User education enabling immediate self-protection. Building on foundational ACP measurement frameworks (Evans, 2025a) and documented failure patterns across major platforms (Evans, 2025b), this blueprint demonstrates how measurable degradation thresholds enable enforceable governance.
Conservative estimates place annual US economic costs at $169 billion from healthcare errors, legal system burden, educational remediation, financial losses, verification overhead, and enterprise incidents. The insurance industry’s retreat from AI coverage signals fundamental market failure requiring regulatory intervention.
The framework provides immediate implementation using existing authority: adverse event registries modeled on pharmaceutical tracking (VAERS, MAUDE), reliability disclosure through sector-specific regulators, and liability frameworks for failures beyond disclosed limits. Legislative templates, three-phase timelines, and jurisdiction-agnostic principles enable action without new legal authority.