Q001 — Query Definition¶


Research	R0044 — Expanded Vocabulary Research
Run	2026-03-29
Query	Q001

Query as Received¶

Using the expanded vocabulary (automation bias, automation complacency, overtrust, overreliance, acquiescence problem, calibrated trust, confirmation bias amplification, alert fatigue, commission error, inappropriate trust), search for enterprise or government requirements, deployment standards, or procurement specifications that constrain AI system behavior — not just human operator behavior — to prevent the system from reinforcing user assumptions or providing agreeable-but-incorrect output. Focus on defense, healthcare, aviation, and financial services.

Query as Clarified¶

Subject: Regulatory and procurement requirements across defense, healthcare, aviation, and financial services
Scope: Requirements that constrain AI system behavior (design, output, interaction patterns) as distinct from requirements that only train or instruct human operators to be vigilant
Evidence basis: Published standards, directives, procurement specifications, regulatory frameworks, and guidance documents from government agencies and regulatory bodies
Key distinction: The query draws a sharp line between system-side constraints (the AI must be designed to do X) and human-side constraints (the operator must be trained to do Y). The focus is on the former.
Vocabulary: Ten specific terms provided as search vectors across four regulated industries

Ambiguities Identified¶

"Constrain AI system behavior" could mean explicit technical requirements (the system SHALL present confidence intervals) or procedural requirements (the system SHALL be tested for bias). The query seems to seek both, but the most interesting finding would be the former — prescriptive design requirements.
System vs. human boundary is not always clean in regulatory language. Many requirements straddle both (e.g., "the system shall be designed to enable effective human oversight" constrains the system but for the purpose of enabling human behavior).
"Agreeable-but-incorrect output" maps to AI safety's "sycophancy" concept but may not appear under that label in regulated-industry standards. The expanded vocabulary provided by the researcher partially addresses this.

Sub-Questions¶

Do any regulated-industry standards explicitly require AI systems to present contradictory evidence, alternative hypotheses, or uncertainty indicators to prevent users from over-trusting outputs?
Do procurement specifications in defense, healthcare, aviation, or finance include testable requirements for AI system behavior related to automation bias prevention?
Is there a meaningful distinction between industries that primarily regulate the human side vs. those that also regulate the system side?
Has the EU AI Act Article 14 created the most explicit system-side requirements, or do sector-specific standards go further?

Hypotheses¶

ID	Hypothesis	Description
H1	System-side requirements exist in regulated industries	Published standards in at least some of the four sectors contain explicit requirements constraining AI system behavior to prevent reinforcing user assumptions
H2	No meaningful system-side requirements exist	Regulated industries focus exclusively on human operator training, supervision, and organizational controls, not on constraining the AI system's own behavior
H3	Requirements exist but are indirect, fragmented, or nascent	Some regulatory language addresses system design but stops short of prescriptive constraints on output behavior; the requirements are emerging, ambiguous, or limited to specific sub-domains