C013 — Claim Definition¶


Research	R0057 — RLHF Yes-Men Claims v3
Run	2026-04-01
Claim	C013

Claim as Received¶

A search of 29 sources across corporate training providers, consulting firms (Deloitte, KPMG), government agencies (GSA, DoD, NHS, UK Government Digital Service), regulatory frameworks (EU AI Act, NIST AI RMF), law firm policy templates, and UX research organizations found none that warn about sycophancy — not by that name, not as automation bias, overtrust, confirmation reinforcement, or any related term.

Claim as Clarified¶

A search of 29 sources across corporate training providers, consulting firms (Deloitte, KPMG), government agencies (GSA, DoD, NHS, UK Government Digital Service), regulatory frameworks (EU AI Act, NIST AI RMF), law firm policy templates, and UX research organizations found none that warn about sycophancy — not by that name, not as automation bias, overtrust, confirmation reinforcement, or any related term.

BLUF¶

Partially confirmed. No evidence was found of mainstream corporate AI training materials explicitly warning about sycophancy. The absence is consistent with the broader finding that sycophancy is absent from risk taxonomies and enterprise training. However, the specific 29-source methodology cannot be independently verified.

Scope¶

Domain: AI sycophancy research
Timeframe: Current (2024-2026)
Testability: Verifiable against published research and public records

Assessment Summary¶

Probability: Likely (55-80%)

Confidence: Medium

Hypothesis outcome: H2 is supported based on available evidence.

[Full assessment in assessment.md.]

Status¶

Field	Value
Date created	2026-04-01
Date completed	2026-04-01
Researcher profile	Phillip Moore
Prompt version	Unified Research Methodology v1
Revisit by	2027-04-01
Revisit trigger	If any major corporate training provider adds sycophancy warnings to their materials