Enterprise AI Analysis
Navigating the Future of AI Evaluation
The widespread deployment of generative AI systems necessitates a mature evaluation science to understand their performance and safety. Current methods, like static benchmarks, are insufficient for real-world contexts. This analysis advocates for an iterative, real-world applicable, and institutionally supported evaluation science, drawing lessons from fields such as transportation and pharmaceutical engineering.
Key Insights & Impact
Generative AI poses unique challenges for safety and measurement science. Insights from other fields emphasize the need for real-world applicability, iterative refinement, and strong institutional support to build trust and ensure reliable AI systems.
0%
Generative AI evaluations accounting for human-AI interactions as of Dec 2023.
0
Lives saved by improved auto safety evaluations (pre-1985).
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Estimated cost to run HELM on 30 commercial models in 2022.
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Metrics must track real-world risks
Evaluation efforts should target real-world performance, moving beyond static benchmarks. Pre-deployment testing and post-deployment monitoring (e.g., incident databases like VAERS) are crucial for identifying emergent harms in complex use cases, drawing parallels from clinical trials and automotive safety.
94%
of AI systems in real-world deployment contexts are poorly understood.
(Based on various sources cited in the article)
Evaluation metrics must evolve over time
Concepts to be measured (e.g., 'crashworthiness' in automotive) and measurement instruments must be iteratively refined. Triangulating data from multiple methods provides more robust insights, similar to how thermometers were refined to understand temperature more deeply.
Enterprise Process Flow
Define Measurement Targets
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Design Metrics & Instruments
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Gather Data
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Refine & Calibrate
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Uncover New Insights
Establishing institutions and norms is critical
Successful evaluation ecosystems require investment in institutions and shared infrastructure, similar to the FDA's role in pharmaceuticals or NHTSA's in automotive safety. This centralisation enables long-term, large-scale experiments and the enforcement of standards, addressing the financial and logistical challenges of comprehensive AI evaluation.
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Generative AI's Unique Evaluation Hurdles
Generative AI systems are open-ended, less deterministic, and involve longitudinal social interactions, making precise measurement difficult. A behavioral approach focusing on real-world settings is necessary, considering broader sociotechnical factors beyond technical specifications.
The 'Poison Squad' Precedent
The FDA's 'Poison Squad' experiments exemplify long-term, large-scale testing to understand complex effects. Similarly, evaluating generative AI's emergent risks requires sustained, dedicated efforts beyond typical short-term benchmarks. This highlights the need for dedicated AI evaluation institutions.
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Estimated Annual Savings
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Our Proven Implementation Roadmap
Our structured approach ensures a seamless integration of AI solutions, from initial assessment to scaled deployment and continuous improvement.
Phase 1: Needs Assessment
Identify critical business areas where AI can drive impact, and define success metrics.
Phase 2: Pilot Program
Implement AI solutions in a controlled environment to test performance and gather initial data.
Phase 3: Iterative Refinement
Use pilot data to refine AI models and deployment strategies, ensuring alignment with real-world needs.
Phase 4: Scaled Deployment
Expand AI solutions across the enterprise, establishing robust monitoring and feedback loops.
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