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Stanford // 2025TOPIC: Safety
The Oversight Game: Learning to Cooperatively Balance an AI Agent’s Safety and Autonomy
AUTHORS: William Overman, Mohsen Bayati | REVIEWED: 2025-01-22
PAPER FINDING
Stanford GSB Working Paper 4309 (arXiv:2510.26752) models the play/ask/trust/oversee framework to balance agent autonomy against human oversight risks.
MY INTERPRETATION
I study how active human verification prompts must be structured to prevent oversight fatigue and rubber-stamping in automated operations.
MY IMPLEMENTATION / CONCEPTUAL APPLICATION
Conceptual application: I designed confidence-threshold verification queues that trigger active human review only when model uncertainty rises.
EVIDENCE & METHODOLOGY
Game-theoretic oversight model review (arXiv:2510.26752).
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 & EDGE CASES
- • Requires calibration to ensure human review prompts do not cause auditor fatigue.