R0043/2026-04-01/Q001
Query: 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."
BLUF: The phenomenon AI researchers call "sycophancy" maps to a rich but fragmented vocabulary across eight domains. No two fields use the same primary term. The terms describe different facets of the same system: sycophancy names the model behavior, automation bias names the human cognitive response, overreliance names the behavioral outcome, and domain-specific terms name the phenomenon as it manifests in each context. A comprehensive map is constructable but reveals related concepts with different causal framings, not simple synonyms.
Probability: N/A (open-ended query) | Confidence: Medium
Summary
| Entity |
Description |
| Query Definition |
Query text, scope, status |
| Assessment |
Full analytical product with reasoning chain and cross-domain vocabulary map |
| ACH Matrix |
Evidence x hypotheses diagnosticity analysis |
| Self-Audit |
ROBIS-adapted 5-domain audit (process + source verification) |
Hypotheses
| ID |
Hypothesis |
Status |
| H1 |
Each domain has specific terminology; comprehensive map constructable |
Supported |
| H2 |
Some domains lack specific terminology, using generic or borrowed terms |
Partially Supported |
| H3 |
Terms describe fundamentally different phenomena, not the same thing under different names |
Partially Supported |
Searches
| ID |
Target |
Results |
Selected |
| S01 |
AI safety sycophancy terminology |
10 |
4 |
| S02 |
Defense/military automation bias terminology |
10 |
2 |
| S03 |
Healthcare acquiescence/deference terminology |
10 |
2 |
| S04 |
Financial, aviation, UX terminology |
30 |
3 |
| S05 |
Cross-cutting overreliance/acquiescence terms |
20 |
2 |
Sources
| Source |
Description |
Reliability |
Relevance |
| SRC01 |
NN/g sycophancy behavioral categories |
High |
High |
| SRC02 |
TechPolicy.Press regressive/progressive taxonomy |
Medium-High |
High |
| SRC03 |
CSET defense automation bias brief |
High |
High |
| SRC04 |
PMC cross-domain vocabulary mapping |
High |
High |
| SRC05 |
Oxford/Cambridge overreliance taxonomy |
High |
High |
| SRC06 |
Braun acquiescence bias counter-finding |
Medium-High |
Medium |
| SRC07 |
IEEE Spectrum engineering perspective |
High |
Medium-High |
| SRC08 |
Aviation human factors terminology |
Medium-High |
High |
| SRC09 |
Cross-domain vocabulary gap analysis |
Medium |
High |
Cross-Domain Vocabulary Map
| Domain |
Primary Terms |
Causal Focus |
| AI Safety |
Sycophancy, reward hacking, sandbagging |
Model behavior |
| Defense/Military |
Automation bias, complacency, overtrust, miscalibrated trust |
Human cognition |
| Healthcare |
Acquiescence, deference, acquiescence problem |
Clinical decision quality |
| Aviation/FAA |
Automation complacency, overtrust/undertrust, use/misuse/disuse/abuse |
Human factors |
| Financial Services |
Model risk, effective challenge, challenge function |
Governance mechanism |
| Academic Integrity |
Confirmation bias amplification (borrowed) |
Detection |
| Enterprise Software |
Hallucination rate, accuracy metrics (borrowed) |
Measurement |
| UX/Product Design |
Satisfaction-accuracy tradeoff, dark patterns (borrowed) |
Design tension |
| Cross-cutting |
Overreliance, appropriate reliance, calibrated trust |
Human behavior |
Revisit Triggers
- Publication of a formal cross-domain taxonomy or glossary by NIST, ISO, or IEEE that includes sycophancy-related terms
- The EU AI Act implementing rules or guidance that name sycophancy or adopt domain-specific alternatives
- DOD or FAA issuing updated human-AI teaming terminology that explicitly addresses LLM-era sycophancy
- Major AI incident in a regulated industry where the vocabulary gap is cited as a contributing factor