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R0043/2026-04-01/Q002

Query: 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.

BLUF: No regulatory framework directly addresses "sycophancy" by name. Four indirect regulatory mechanisms provide partial coverage: EU AI Act (automation bias awareness), NIST AI 600-1 (confabulation/information integrity), SR 11-7 (effective challenge), and FDA (human factors evaluation). The gap is specifically at the intersection of model behavior and regulatory language — regulations address human responses and output quality but not the model tendency to prioritize agreement over accuracy.

Probability: N/A (open-ended query) | Confidence: Medium


Summary

Entity Description
Query Definition Query text, scope, status
Assessment Full analytical product with regulatory coverage map
ACH Matrix Evidence x hypotheses diagnosticity analysis
Self-Audit ROBIS-adapted 5-domain audit

Hypotheses

ID Hypothesis Status
H1 Direct sycophancy requirements exist in regulated industries Eliminated
H2 No requirements address the phenomenon at all Eliminated
H3 Indirect coverage exists but does not name the model behavior Supported

Searches

ID Target Results Selected
S01 Regulatory standards across 4 industries 40 6
S02 Enterprise procurement specifications 10 0

Sources

Source Description Reliability Relevance
SRC01 EU AI Act Article 14 High High
SRC02 NIST AI 600-1 High Medium-High
SRC03 SR 11-7 effective challenge High Medium-High
SRC04 FDA AI device guidance High Medium
SRC05 IEEE 3119 procurement High Medium
SRC06 Georgetown regulatory gap analysis Medium-High High

Regulatory Coverage Map

Regulation/Standard What It Addresses What It Misses (re: Sycophancy)
EU AI Act Art. 14 Human automation bias awareness Model behavior that induces automation bias
NIST AI 600-1 Confabulation, information integrity Agreement-seeking (vs. factual errors)
SR 11-7 Independent validation, effective challenge Model tendency to resist challenge
FDA AI guidance Human factors, accuracy metrics Acquiescence/deference in clinical AI
IEEE 3119 Procurement process structure Sycophancy evaluation criteria

Revisit Triggers

  • EU AI Act implementing rules or guidance published that specify sycophancy testing
  • NIST AI RMF or 600-1 updated to include sycophancy or agreement-seeking as a risk category
  • SR 11-7 supplement or update addressing generative AI model behaviors
  • FDA finalization of AI/ML device guidance with sycophancy provisions
  • Major AI incident in a regulated industry attributed to sycophantic model behavior