R0044/2026-03-29/Q004/H3¶
Statement¶
CaTE examines AI system design properties (reliability, transparency, explainability) that enable trust calibration, but does not address the system actively adjusting its output to match or counteract user expectations.
Status¶
Current: Supported
This is the best-supported hypothesis. CaTE's published work — the Guidebook and companion guides — addresses system design properties (trustworthiness dimensions) and human trust measurement (calibration methods). It examines "the dynamics of how systems interact with each other, and especially the interactions between AI and humans." However, it does not address AI systems modifying their own output based on detected user trust or to prevent sycophancy. The framework is about measuring appropriate trust levels, not about constraining AI system behavior to prevent trust miscalibration at the output level.
Supporting Evidence¶
| Evidence | Summary |
|---|---|
| SRC01-E01 | CaTE Guidebook covers trust, trustworthiness, calibrated trust, and ethics based on practices, frameworks, and metrics |
| SRC02-E01 | CaTE develops standards, methods, and processes for providing evidence for assurance and calibrated trust measures |
| SRC03-E01 | Sandia TCMM (related work) focuses on communicating trustworthiness, not constraining AI behavior |
Contradicting Evidence¶
No evidence contradicts H3.
Reasoning¶
CaTE occupies the middle ground: it goes beyond purely human-focused trust measurement (eliminating H2 as too narrow) by also examining system design properties that enable appropriate trust. But it stops short of addressing system output behavior (eliminating H1). The gap is precisely the sycophancy question — CaTE asks "is this system trustworthy?" not "does this system adjust its output to appear more trustworthy than it is?"
Relationship to Other Hypotheses¶
H3 is the most precise characterization. H1 overestimates CaTE's scope (no system output behavior). H2 underestimates it (CaTE does address system properties, not just human trust).