Q004 — Query Definition¶


Research	R0044 — Expanded Vocabulary Research
Run	2026-04-01
Query	Q004

Query as Received¶

The DoD CaTE (Calibrated AI Trust and Expectations) center was identified as having the most sophisticated regulated-industry vocabulary for this problem. What has CaTE published about calibrating trust in AI systems, and does their work address the system-side behavior (AI adjusting output to match user expectations) or only the human-side behavior (users trusting AI too much)?

Query as Clarified¶

This query asks two things: (1) what CaTE has published, and (2) whether CaTE's work addresses system-side behavior or only human-side behavior. The query contains an embedded claim that CaTE has "the most sophisticated regulated-industry vocabulary" — this is tested against the evidence rather than assumed.

Note: The query refers to "Calibrated AI Trust and Expectations" but CaTE actually stands for "Calibrated Trust Measurement and Evaluation" per official sources. This correction is noted.

BLUF¶

CaTE has published one primary deliverable: a guidebook for the development and TEVV (Testing, Evaluation, Verification, and Validation) of LAWS (Lethal Autonomous Weapons Systems), authored by Mellinger, Brooks, Fairfax, and Justice (April 2025). CaTE's stated scope covers both system-side ("standards, methods, and processes for providing evidence for assurance") and human-side ("evaluating operator trust") — but the available evidence indicates the emphasis is predominantly on human-side trust measurement and system trustworthiness evaluation, not on constraining AI output behavior. CaTE does not use sycophancy vocabulary. Its sophistication lies in the "calibrated trust" concept and the human-machine teaming framework, not in addressing AI agreement behavior.

Scope¶

Domain: DoD AI trust and assurance, lethal autonomous weapons systems
Timeframe: 2023-2026
Testability: Verifiable by examining CaTE publications and stated mission

Assessment Summary¶

Probability: N/A (open-ended query)

Confidence: Medium

Hypothesis outcome: H2 (CaTE addresses both sides but emphasizes human-side) is best supported.

[Full assessment in assessment.md.]

Status¶

Field	Value
Date created	2026-04-01
Date completed	2026-04-01
Researcher profile	Not provided
Prompt version	Unified Research Methodology v1
Revisit by	2027-01-01
Revisit trigger	CaTE publication of additional guidebooks or standards; CaTE adoption of AI safety vocabulary