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Stanford // 2025TOPIC: SafetyTechnical Note
The Oversight Game: Learning to Cooperatively Balance an AI Agent’s Safety and Autonomy
AUTHORS: William Overman, Mohsen Bayati | PUBLISHED & REVIEWED: 2025-01-22
CORE ARCHITECTURAL THESIS & FINDING
Stanford GSB Working Paper 4309 (arXiv:2510.26752) models the play/ask/trust/oversee framework to balance agent autonomy against human oversight costs and rubber-stamping risks.
MY INTERPRETATION
Human verification prompts must be structured strategically based on model uncertainty to prevent operator fatigue and automation bias.
PRACTICAL IMPLEMENTATION
Designed confidence-threshold verification queues that trigger active human review only when model uncertainty rises above calibrated thresholds.
RETRIEVAL_INTELLIGENCE // HYBRID_RAGVECTOR RETRIEVAL & RERANKING
STEP 01
Chunking
512 token splits
STEP 02
Embedding
Dense vectors
STEP 03
Qdrant Search
Cosine sim (k=25)
STEP 04
Cross-Encoder
Rerank top-5
STEP 05
Grounded Gen
With citations
SYSTEM LIMITATIONS, RUNTIME OVERHEAD & PRODUCTION CONSTRAINTS
- •Requires careful calibration to ensure human review prompts do not cause auditor fatigue.
EVIDENCE & REPRODUCIBILITY METHODOLOGY
Game-theoretic oversight model review (arXiv:2510.26752).