State of AI Safety Report 2024

Executive Summary

As we enter 2026, the United States faces a regulatory fracture. The incoming Administration's draft Executive Order seeking to preempt state AI laws has created a vacuum of uncertainty. We are caught between a "Wild West" federal approach (prioritizing speed) and a "Patchwork" state approach (prioritizing safety).

This White Paper argues that neither approach is sustainable. "Deregulation" ignores the product defects of AI (hallucinations and bias), while "State Patchworks" stifle innovation. We propose a Third Way: A Federal Product Safety Standard based on Constitutional AI—replacing ideological debates with engineering constraints.

1. The Current Landscape: A House Divided

The Federal Pivot: "Dominance over Safety"

The 2025 "America's AI Action Plan" and the draft Executive Order on "Eliminating State Law Obstruction" signal a definitive shift. The federal government is moving to:

• Preempt State Laws: Threatening to withhold broadband funding from states like California and Colorado that enforce AI safety rules.
• Ban "DEI" in Code: Instructing the OMB to reject models with "ideological biases," effectively politicizing the concept of algorithmic fairness.
• Goal: To outpace China by removing "burdensome" guardrails.

The State Counter-Move: "The California Effect"

Despite the veto of SB 1047, California remains the de facto global regulator. With federal oversight retreating, states are stepping in to enforce:

• Transparency: Mandating disclosure of training data.
• Civil Rights: Applying existing anti-discrimination laws (Unruh Act) to algorithmic decision-making.

The Risk: A constitutional crisis where AI developers are frozen by conflicting mandates—sued by the Feds if they do filter for bias, and sued by the States if they don't.

2. The Engineering Reality: Why "Deregulation" Fails

The federal push for "Minimal Regulation" is based on a misunderstanding of how Large Language Models (LLMs) work. It assumes that an unconstrained model is "neutral."

The Mirror Effect:

Our research demonstrates that unconstrained models are not neutral. They are Mirrors of historical human data.

• Human data is full of "Decision Fatigue," "Cognitive Bias," and "Historical Discrimination."
• Therefore, a "deregulated" AI does not produce objective truth; it automates human error. It creates a product that is legally defective by design.

Conclusion: You cannot achieve "AI Dominance" with defective software. Safety constraints are not "woke"; they are quality control.

3. The Solution: Constitutional AI as the Federal Standard

To resolve the conflict between "Innovation" and "Safety," we must move beyond "Human-in-the-Loop" (which is slow) to "Constitution-in-the-Code."

The Framework

We propose a unified federal standard where AI models must adhere to Explicit Normative Constraints:

• The Constitution: A set of hard-coded rules (e.g., "Do not discriminate based on protected class," "Prioritize factual accuracy over user flattery").
• The Override: These rules must override training data. If the history says "Deny Loan," but the Constitution says "Fair Access," the Constitution wins.

Why this Solves the Political Gridlock

• For Republicans: It frames safety as "Rule of Law" and "Accuracy," not "Social Engineering." It replaces vague "Fairness" metrics with specific "Constitutional Compliance."
• For Democrats: It provides a hard brake on algorithmic discrimination that is more effective than "transparency" reports.
• For Industry: It provides a single, predictable standard (The Constitution) rather than 50 state laws.

4. Recommendations for the 119th Congress

Conclusion

We are at a crossroads. We can either allow AI to become a chaotic mirror of our past mistakes, or we can engineer it to reflect our constitutional ideals. The "Third Way" is not to stop the machine, but to give it a Constitution.