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R0043 — Sycophancy Vocabulary

Mode: Query · Status: Active · Tags: AI sycophancy, vocabulary, taxonomy, cross-domain terminology

Input

  1. What terms do different industries and disciplines use to describe AI behavior that prioritizes user agreement, comfort, or satisfaction over accuracy, correctness, or safety? Map the complete vocabulary across: AI safety research, defense/military AI, healthcare AI, financial services AI, aviation/FAA, academic integrity, enterprise software evaluation, and UX/product design. For each domain, identify the specific terms used for the phenomenon that AI researchers call "sycophancy."
  2. Using the vocabulary identified in Q1, search for enterprise requirements, procurement specifications, regulatory guidance, or deployment standards that address the sycophancy phenomenon under its domain-specific names. Focus on regulated industries (defense, healthcare, finance, aviation) where agreeable-but-wrong AI output could cause harm.
  3. Has the vocabulary gap itself been identified as a problem in the AI safety or AI governance literature? Are there researchers or organizations working to create a shared taxonomy that bridges AI safety terminology with regulated-industry terminology?

Runs

2026-03-28 — Initial research run

Mode: Query · Queries: 3 · Prompt: Unified Research Methodology v1 · Model: Claude Opus 4.6

First investigation of sycophancy vocabulary across 8 domains.

2026-04-01 — Rerun (isolation-compliant)

Mode: Query · Queries: 3 · Prompt: Unified Research Methodology v1 · Model: Claude Opus 4.6 (1M context)

Isolation-compliant rerun. Confirmed fragmented vocabulary with no shared primary term across domains. Key addition: Roytburg & Miller network analysis showing 83% homophily between AI safety and ethics communities as structural explanation for terminology diffusion failure.