The Artificial Intelligence Benchmark: The Most Important Clause You’ve Never Used (Part 1) | Shumaker, Loop & Kendrick, LLP

Here’s a summary of the article on benchmarking importance in AI contracts:

Key Role of Benchmarking in AI Contracts:
- Benchmark testing bridges aspirational AI promises and measurable, enforceable performance.
- Contracts without robust testing requirements rely on unverifiable claims of AI performance.
Importance of Consistent AI Performance:
- AI performance depends on context, and “real world” data is rarely used in demos.
- The AI model may not produce the same results after deployment as during tests.
Need for Performance Stability:
- AI systems change due to vendor updates and changes in user environments.
- Without robust metrics, detecting performance drifts fall on the user’s responsibility.
Critical Benchmark Tests for Different AI Types:
- Foundational Generative AI: accuracy, hallucinations, instruction following, confidentiality
- Retrieval-based AI: citation correctness, grounding, recency controls, access controls
- Predictive AI: precision/recall, calibration, bias/fairness, stability, explainability
- Agentic AI: tool-use correctness, permission boundaries, safety constraints, auditability, adversarial resilience, kill-switch and rollback
Consequences of Inadequate Benchmarking:
- Operational failures resulting in business process errors and costly manual corrections.
- Legal and regulatory issues leading to potential violations of consumer protection laws and anti-discrimination statutes.
- Potential leaks of confidential information by AI through misconfiguration or malicious inputs.
- Downstream risks like inaccurate or biased output leading to policy violations or record integrity issues.

This summary highlights why benchmarking is integral to AI contracts across different use cases and the critical role it plays in ensuring reliable and safe AI deployment.