arXiv:2602.07840v3 Announce Type: replace-cross Abstract: Evaluating relevance in large-scale search systems is fundamentally constrained by the governance gap between nuanced, resource-constrained human oversight and the high-throughput requirements of production systems. While traditional approaches rely on engagement proxies or sparse manual review, these methods often fail to capture the full scope of high-impact relevance failures. We present \textbf{SAGE} (Scalable AI Governance \& Evaluation), a framework that operationalizes high-quality human product judgment as a scalable evaluation
The proliferation of AI systems across critical applications necessitates robust and scalable evaluation mechanisms to ensure their responsible deployment and continuous improvement.
A framework like SAGE addressing the 'governance gap' for large-scale AI evaluation is critical for practical and responsible AI adoption, especially as AI systems become more autonomous and impactful.
The ability to operationalize high-quality human judgment at scale for AI evaluation significantly improves the reliability and safety of large-scale AI applications, moving beyond inadequate reliance on proxies or sparse reviews.
- · AI developers
- · AI governance platforms
- · High-throughput AI systems
- · AI safety researchers
- · AI systems with poor evaluation protocols
- · Organizations relying solely on proxy metrics
- · Ad-hoc AI governance solutions
Improved trust and adoption of AI technologies across various industries due to enhanced reliability.
Increased investment in specialized AI evaluation and governance tools and services, fostering a new sub-sector within the AI industry.
Potentially, more stringent regulatory standards for AI deployment, as practical evaluation methodologies become available for enforcement.
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Read at arXiv cs.AI