Properties of Formal Market Driven Mechanism

• Formal Guarantees(Formally Safe Verification) [Ex Ante]: Utilizing mathematical models and formal methods to rigorously prove that AI systems operate within specified safety and ethical boundaries. This approach aims to provide objective assurances about AI behavior, reducing reliance on trust and increasing transparency
• Creation of Countermeasures[Ex Nunc]: Developing tools and techniques to prevent, detect, and mitigate potential misuse or harmful outcomes of AI systems, thereby enhancing their resilience against malicious interventions. This includes discouraging dual [NIST] applications and human augmentation in high-risk domains. Implementation of safety parameters [ If-Then Commitments for AI Risk Reduction]
• System Restoration Mechanisms [Post Ante] : Implementing methods to eliminate undesired AI behaviors or components, effectively restoring systems to their intended operational states. This involves designing self-elimination mechanisms that allow AI systems to autonomously remove or neutralize harmful elements, ensuring alignment with ethical standards and legal frameworks. Implementing reversal mechanisms is also essential to address large-scale societal impacts, allowing for the mitigation of unintended consequences and the restoration of societal norms and values.

[Dobrev, Formal Definition of AI ]

Properties of Formally Safe & Flexible Hardware (Heilmeier Catechism for flexHEGS)

Heilmeier Catechism– a set of questions proposed by DARPA to help agencies evaluate grant proposals.Wasil et al. propose a flexible hardware-enabled governance mechanism (flexHEG) for AI chips, aiming to enforce AI-related rules at the hardware level (Wasil et al., 2024).** The system would allow on-chip compliance enforcement, real-time rule updates, and tamper-resistant security. The research focuses on two workstreams:

(1) implementing and updating governance rules and
(2) ensuring hardware security through tamper-evidencing enclosures and self-disablement mechanisms.

Key risks include hardware security feasibility, adoption by industry, and performance trade-offs. The proposal estimates a $15M-$30M budget over four years, with a mid-term prototype ($4M-$8M in two years) and final technological maturity ($15M-$30M in 3-5 years).

Metrics for success:

• Tamper Resistance Score: Evaluating chip security under penetration testing.
• Rule Compliance Rate: Ensuring unauthorized AI operations are blocked.
• Performance Overhead Benchmark: Targeting a ≤4% slowdown in AI chip performance(compared to SOTA AI Chips without flexHEG).

The feasibility of flexHEGs depends on balancing security with industry adoption, ensuring enforcement mechanisms are both effective and economically viable.

Final outcomes for flexHEGs would provide full technical specifications, standards, and open-source libraries to ensure seamless industry adoption. This includes:

• Secure processor capable of enforcing complex compliance rules (e.g., adaptive FLOP-based evaluations in zero-knowledge) and securing AI-related data through encryption.
• K-over-N updating mechanism (K/N) to allow flexible rule updates.
• Compliance-compute interlock that integrates with next-gen AI chips without performance degradation.
• Feature requests for modifying accelerator firmware and kernels to support flexHEGs.
• Robust hardware security, validated through high-stakes bounty programs and national lab testing.

Formal Markets – Advanced Market Commitment (AMC) for frontier AI

Formal markets involve developing rigorous mathematical models, cryptography and policies to verify that AI systems adhere to specified properties, providing a foundation for predictable and reliable outputs and social impacts.

This approach offers benefits such as autoformalization[Cunningham] , and measurable societal advantages [S. Russell, Provably Beneficial AI], both intrinsic and extrinsic and Baum [Baum]

Type I: Formal Guarantees (Pull) – Verified AI & Compute Safety
Ensuring AI is reliable, and trustworthy [Sifakis] necessitates a multifaceted approach that encompasses formal mathematical reasoning, hardware-based governance mechanisms, and contract-based frameworks, described below:

• Hardware-based governance mechanisms integrate safety protocols directly into AI hardware, enabling on-chip verification and enforcement of safety constraints, which is crucial for preventing unintended behaviors in critical applications.
• Contract-based frameworks, such as assume/guarantee contracts, facilitate modular verification by abstracting system components, allowing for structured and scalable safety assessments. Collectively, these strategies contribute to a comprehensive safety assurance framework for AI systems, addressing both theoretical and practical challenges in the pursuit of trustworthy AI.

Flexible Hardware - Flexible Hardware Enabled Guarantees (FlexHEGs)

SFF-2024 recommended about $1-4M to the work on Mechanisms for Flexible Hardware-Enabled Guarantees [flexHegs]

• Objective: Ensure AI compute systems are adaptive, verifiable, and resilient to novel use cases.
  The "Mechanisms for Flexible Hardware-Enabled Guarantees (FlexHEG) Interim Report" outlines several design and policy choices aimed at enhancing AI governance through hardware-based mechanisms:
• Design Choices:
  Monitoring Capabilities: Equip GPUs with inputs to monitor usage effectively, facilitating compliance with governance policies.
  Cryptographic Integrity: Integrate a time-dependent [Döttling, Verifiable Delay Functions] cryptographic primitives into GPUs and tools cryptographic hardware to secure data, operations, access and to support as governance mechanisms.
• Policy Choices:
  Multilateral Governance: Enable diverse stakeholders to collaboratively establish and update governance rules, ensuring broad consensus and adherence.
  Cryptographically Secure Rule Updates: Utilize cryptographic mechanisms to securely implement and verify updates to governance rules, maintaining integrity and trust.

Formal Methods & Mathematics for Safe AI

• Provable Safe Contracts: Develop formal proofs to ensure AI systems’ alignment, robustness, and security.
• Meta-Contracts for Compute: Implement contracts ensuring AI hardware adheres to strict compliance and control specifications, including enforcement mechanisms for export controls and legal compliance.
  Provable Meta-Contracts (PMCs) establish formal constraints on the creation and modification of other provable contracts, ensuring compliance with specified rules and regulations. They can enforce adherence to legal requirements and ethical standards by verifying cryptographic signatures. In this framework, a provable contract governs the hardware that a Provably Correct Computing (PCC) system utilizes, ensuring that the hardware complies with predefined specifications and export control regulations. [Tegmark ,Provably safe systems]
• Corrigibility, Differentiality & Interpretability:
• Design AI systems to be responsive to human intervention and correction. eg. Utilize feature tuning via Sparse Autoencoders (SAEs) to extract human-interpretable models.
• Mathematical Guarantees: Incentivize formal verification methods to ensure AI alignment. [ARIA UK, Mathematics for Safe AI]