MCAI National Innovation Vision: Comment on Regulatory Reform on Artificial Intelligence
A Notice by the Office of Science and Technology Policy, Docket ID OSTP-TECH-2025-0067
MindCast AI comment on Office of Science and Technology Policy Notice of Request for Information; Regulatory Reform on Artificial Intelligence (Docket ID OSTP-TECH-2025-0067).
I. Executive Summary
Artificial Intelligence systems now assist in decisions once reserved for people, yet most federal regulations were written for static, human-operated processes. When applied to adaptive, continuously learning systems, these frameworks can slow innovation or erode accountability.
This comment recommends that agencies employ predictive-simulation analysis and causal-traceability metrics to evaluate proposed AI rules before adoption. These foresight tools—already common in aviation and energy safety—help policymakers identify unintended consequences early and preserve public trust in AI oversight. MindCast AI LLC, a U.S. developer of predictive-simulation techniques, submits these observations to support OSTP’s request for information.
Definitions.
Cognitive Digital Twin – a decision-process simulation that models how an agency or enterprise makes choices.
Foresight-based regulatory testing – running such simulations before a rule is finalized to reveal operational bottlenecks or risk.
Predictive Cognitive AI – analytical systems that forecast institutional behavior rather than automate tasks.
These describe general capabilities already used in sectors such as energy reliability and flight safety.
Why Now.
Addressing these barriers advances the America’s AI Action Plan (2025) directive to secure U.S. leadership in safe, beneficial AI. The European Union’s prescriptive AI Act and China’s accelerated deployment show that regulatory agility has become a strategic variable in competitiveness. Modernizing U.S. rule design through predictive testing and accountability metrics would position America as a standard-setter rather than a follower.
Contact mcai@mindcast-ai.com to partner with us on AI policy and national innovation policy. See also MCAI Innovation Vision: The Commerce Clause as America’s AI Advantage (Sep 2025), The AI Duel of America’s Chaotic Advantage vs. China’s Disciplined Coordination (Aug 2025), How the U.S. Can Foster AI Innovation Using Intellectual Property as a National Innovation System (Aug 2025), America Needs a National AI Revolution (Aug 2025), The Federal Unification of Intelligence, AI Preemption and the Rise of National Foresight (Jun 2025).
II. Concrete Example of Current Barriers
A 2024 FDA-cleared imaging model trained on adaptive datasets waited 11 months for re-approval after a minor accuracy update because validation rules assumed a fixed, human-operated device. The delay cost more than $5 million in compliance and deferred improved cancer detection for thousands of patients. Similar bottlenecks appear in transportation, finance, and infrastructure, where adaptive AI requires re-approval after each learning cycle. Predictive-simulation validation could have shown regulatory equivalence within weeks, preserving both safety and timeliness.
III. Regulatory Barriers and Potential Reforms
Testing and Certification Mismatch
Current approval cycles assume static products. Adaptive AI evolves after deployment. Agencies could supplement traditional testing with predictive-simulation validation that measures safety, fairness, and reliability across potential future data conditions. Outdated cycles can add 6–18 months to deployment; with average development costs near $1 million per month, delays impose heavy burdens on innovators and end users alike.Liability and Accountability Gap
Existing law assigns responsibility only to human actors. In mixed human–AI systems, accountability can diffuse. Agencies could adopt causal-traceability metrics to ensure every automated action remains linked to an accountable human or institutional steward.Such metrics might integrate (a) alignment between policy intent and execution, (b) reliability of system behavior, and (c) quality of oversight—adjusted for complexity. For example, a traceability metric could confirm that when an AI system denies a loan application, the decision path connects to a specific credit policy approved by a compliance officer, with a documented rationale available for review. One private-sector illustration, sometimes called a “causal-signal-integrity” model, quantifies these relationships; technical specifications can be shared separately if useful.
Human-Oversight Mandates
“Always-on” supervision rules often create nominal oversight rather than effective control. Regulators should emphasize coherence—whether a system can explain its behavior—and recoverability—its ability to detect and correct errors faster than manual review. These measurable outcomes preserve accountability while reducing administrative burden.Data-Practice Rigidity
Privacy and provenance rules designed for static datasets impede lawful recursive learning. Agencies could update data-governance provisions to require auditable data-lineage logs that record how training and updates occur while safeguarding confidentiality.
IV. Patent and Intellectual-Property Considerations
Barrier – Human-Inventorship Assumptions
The Patent Act (35 U.S.C. §§ 100–103) and USPTO rules (37 C.F.R. §§ 1.41–1.46) presume inventors are natural persons. This discourages disclosure of AI-assisted discoveries and shifts innovation toward trade secrecy.
Recommended Actions
Issue guidance recognizing AI-enabled inventive contributions made under accountable human stewardship.
Create an AI-assisted examination pilot under 35 U.S.C. § 2(b)(2) to test predictive-simulation tools that forecast claim overlap before patent grant.
Update disclosure rules (37 C.F.R. § 1.56) to include relevant system parameters and data provenance that materially affect invention.
These reforms preserve human accountability while acknowledging machine-enabled creativity and reinforce the transparency purpose of U.S. patent law.
V. Administrative Tools Underused
Agencies already possess flexible authorities—waivers, exemptions, pilot programs, and experimental rulemaking under 5 U.S.C. §§ 301 and 553(e)—that could lawfully accommodate AI experimentation. OSTP could coordinate an inter-agency Regulatory Foresight Testbed enabling participants to:
Run predictive-simulation stress tests of proposed AI rules;
Evaluate causal accountability using traceability metrics; and
Publish findings to improve transparency and stakeholder confidence.
This approach would turn regulatory foresight into measurable administrative practice.
VI. Recommendations and Path Forward
To modernize AI governance without sacrificing accountability, this comment recommends that federal agencies:
Establish a Regulatory Foresight Testbed — Use existing pilot authorities to model and stress-test proposed AI rules before adoption.
Adopt Causal-Traceability and Coherence Metrics — Ensure clear human accountability and measurable system reliability across agencies.
Modernize Patent and Data Governance Frameworks — Accommodate AI-enabled invention and adaptive learning while maintaining transparency and public trust.
Implementation Considerations.
Foresight testing complements, rather than replaces, existing oversight. Agencies can conduct periodic assessments ensuring AI systems remain aligned with original policy objectives as they learn. Integrating such assessments within pilot programs demonstrates that automation continues to serve human intent over time.
Public trust depends on predictive accountability: the capacity to foresee, quantify, and mitigate error before harm occurs. Simulation-based foresight provides that capacity while keeping decision authority traceable to human governance. Embedding these practices through a Regulatory Foresight Testbed would operationalize the America’s AI Action Plan goal of ensuring that innovation proceeds safely, competitively, and in the public interest.
Respectfully submitted,
Noel Le
Founder & Architect, MindCast AI LLC