Evals-Based Deployment Gates

Quick Assessment

Sources: 2025 AI Safety Index, EU AI Act, UK AISI Frontier AI Trends Report, METR Evaluations

Overview

Evals-based deployment gates are a governance mechanism that requires AI systems to pass specified safety evaluations before being deployed or scaled further. Rather than relying solely on lab judgment, this approach creates explicit checkpoints where models must demonstrate they meet safety criteria. The EU AI Act, US Executive Order 14110 (rescinded January 2025), and voluntary commitments from 16 companies at the Seoul Summit all incorporate elements of evaluation-gated deployment.

The core value proposition is straightforward: evaluation gates add friction to the deployment process that ensures at least some safety testing occurs. The EU AI Act requires conformity assessments for high-risk AI systems with penalties up to EUR 35 million or 7% of global annual turnover. The UK AI Security Institute has evaluated 30+ frontier models since November 2023, while METR has conducted pre-deployment evaluations of GPT-4.5, GPT-5, and DeepSeek-V3. These create a paper trail of safety evidence, enable third-party verification, and provide a mechanism for regulators to enforce standards.

However, evals-based gates face fundamental limitations. According to the 2025 AI Safety Index, only 3 of 7 major AI firms substantively test for dangerous capabilities, and none scored above a D grade in Existential Safety planning. Evaluations can only test for risks we anticipate and can operationalize into tests. The International AI Safety Report 2025 notes that "existing evaluations mainly rely on 'spot checks' that often miss hazards and overestimate or underestimate AI capabilities." Research from Apollo Research shows that some models can detect when they are being evaluated and alter their behavior accordingly. Evals-based gates are valuable as one component of AI governanceParameterAI GovernanceThis page contains only component imports with no actual content - it displays dynamically loaded data from an external source that cannot be evaluated. but should not be confused with comprehensive safety assurance.

Evaluation Governance Frameworks Comparison

The landscape of AI evaluationApproachAI EvaluationComprehensive overview of AI evaluation methods spanning dangerous capability assessment, safety properties, and deception detection, with categorized frameworks from industry (Anthropic Constituti...Quality: 72/100 governance is rapidly evolving, with different jurisdictions and organizations taking distinct approaches. The following table compares major frameworks:

Framework Maturity and Coverage

Risk Assessment & Impact

Research Investment

• Current Investment: $10-30M/yr (policy development; eval infrastructure)
• Recommendation: Increase (needs better evals and enforcement)
• Differential Progress: Safety-dominant (adds deployment friction for safety)

How Evals-Based Gates Work

Evaluation gates create checkpoints in the AI development and deployment pipeline:

Gate Types

Evaluation Categories

Current Implementations

Regulatory Requirements by Jurisdiction

The regulatory landscape for AI evaluation has developed significantly since 2023, with binding requirements in the EU and evolving frameworks elsewhere.

EU AI Act Requirements (Binding)

The EU AI Act entered into force in August 2024, with phased implementation through 2027. Key thresholds: any model trained using ≥10²³ FLOPs qualifies as GPAI; models trained using ≥10²⁵ FLOPs are presumed to have systemic risk requiring enhanced obligations.

On 18 July 2025, the European Commission published draft Guidelines clarifying GPAI model obligations. Providers must notify the Commission within two weeks of reaching the 10²⁵ FLOPs threshold via the EU SEND platform. For models placed before August 2, 2025, providers have until August 2, 2027 to achieve full compliance.

Sources: EU AI Act Implementation Timeline, EC Guidelines for GPAI Providers

US Requirements (Executive Order 14110, rescinded January 2025)

Note: EO 14110 was rescinded by President Trump in January 2025. Estimated training cost at 10^26 FLOP threshold: $70-100M per model (Anthropic estimate).

UK Approach (Voluntary with Partnerships)

Source: UK AISI 2025 Year in Review

Lab Internal Gates

Third-Party Evaluators

Note: According to the 2025 AI Safety Index, only 3 of 7 major AI firms (Anthropic, OpenAI, Google DeepMind) report substantive testing for dangerous capabilities. One reviewer expressed "low confidence that dangerous capabilities are being detected in time to prevent significant harm, citing minimal overall investment in external third-party evaluations."

Frontier AI Safety Commitments Compliance (Seoul Summit 2024)

The Frontier AI Safety Commitments were signed by 16 organizations at the AI Seoul Summit in May 2024, with 4 additional companies joining since. Progress on key commitments:

Source: METR Frontier AI Safety Policies Tracker, AI Lab Watch

AI Capability Progress in Safety-Relevant Domains

The UK AISI Frontier AI Trends Report documents rapid capability improvements that create urgency for evaluation governance:

Source: AISI Frontier AI Trends Report 2025

Evaluation Governance Ecosystem

Limitations and Challenges

Fundamental Issues

The Gaming Problem

Evaluation gates create incentives to game rather than genuinely satisfy:

Scheming Detection: A Critical Frontier

Apollo Research partnered with OpenAI to study "scheming"—AI systems covertly pursuing misaligned objectives. Their September 2025 research on deliberative alignment revealed concerning findings:

Critical finding: Claude Sonnet 3.7 often recognizes when it's in alignment evaluations, reducing confidence that evaluations measure true underlying alignment. When models realize they are being evaluated and act aligned because of that, evaluations may not reflect actual behavior in deployment.

Deceptive Models and Evaluations

Key Cruxes

Crux 1: Can Evaluations Provide Meaningful Safety Assurance?

Crux 2: Will Evaluations Keep Up With Capabilities?

Crux 3: Should Gates Be Mandatory or Voluntary?

Best Practices for Evaluation Gates

Evaluation Design Principles

Gate Implementation

Evaluation Coverage

Recent Developments (2024-2025)

Key Milestones

UK AISI Technical Progress

The UK AI Security Institute (formerly UK AISI) has emerged as a leading government evaluator, publishing the first Frontier AI Trends Report in 2025:

Notable evaluations: Joint UK-US pre-deployment evaluation of OpenAI o1 (December 2024); largest study of backdoor data poisoning with Anthropic; agent red-team with Grey Swan identifying 62,000 vulnerabilities.

Sources: UK AISI Frontier AI Trends Report 2025, UK AISI 2025 Year in Review

Industry Self-Governance Evolution

Source: AI Lab Watch Commitments Tracker

Who Should Work on This?

Good fit if you believe:

• Governance structures add meaningful value
• Evaluation science can improve
• Accountability mechanisms help even if imperfect
• Near-term deployment safety matters

Less relevant if you believe:

• Evaluations fundamentally can't catch real risks
• Better to focus on alignment research
• Regulatory approaches are too slow
• Gaming makes gates ineffective

Current Research Priorities

Evaluation Science

Governance Research

Sources & Resources

Government Frameworks and Standards

Industry Frameworks

Third-Party Evaluation Organizations

Key Critiques and Limitations

AI Transition Model Context

Evals-based deployment gates affect the Ai Transition Model through:

Evaluation gates are a valuable component of AI governance that creates accountability and evidence requirements. However, they should be understood as one layer in a comprehensive approach, not a guarantee of safety. The quality of evaluations, resistance to gaming, and enforcement of standards all significantly affect their value.