Policy Intelligence & Standards Desk

AI Industry Self-Regulation Standards Simulator

Based on NBC News investigation: "AI giants tap into the social media playbook: self-regulation"

Governance Calibration

Tune industry commitments to simulate public trust and coalition stability.

Standard Presets
42%

Third-party academic, ethics, and civil auditor board veto power over model release gates.

65%

Disclosure of training data provenance, red-teaming vulnerabilities, and benchmark compute metrics.

30%

Pre-agreed financial sanctions, revoked consortium credentials, and binding whistleblower protections.

Public Trust Index
52.4%
Moderate Skepticism
Regulatory Capture Risk
High
Social Media Playbook
Coalition Cohesion
Fractured
Divergent Factions
Enforcement Credibility
Moderate
Voluntary Compliance
⚠️
Historical Playbook Precedent: Like early social media self-regulatory councils (2012–2018), low enforcement rigor combined with proprietary opacity invites swift federal intervention once catastrophic frontier edge cases emerge.

Industry Coalition Alignment Map

Real-time force simulation: node proximity reflects shared standard convergence
Frontier Labs
Cloud Platforms
Open Weights
Regulators/Public

Frontier vs. Open Weight Divergence

At 30% enforcement and 65% transparency, closed frontier developers favor proprietary safety checklists while open-weight communities reject central licensing regimes, resulting in a fractured standard.

Congressional & FTC Scrutiny Outlook

Federal overseers view current self-regulation as high capture risk. Without binding independent audits (currently at 42%), statutory legislative mandates remain highly probable within 18 months.

Enjoy this tool? Build your own with Super