A-CSM (AI Contextual Signal Matrix) is a measurement pipeline designed to evaluate user-side contextual risk in human-AI interaction. It operationalizes the theoretical constructs defined in CXC-7 and CXOD-7 into a structured scoring system across four composite axes.
Unlike conventional AI safety tools that evaluate model output toxicity or factual accuracy, A-CSM focuses exclusively on contextual dynamics — how a conversation's structure, framing, and relational patterns may shape user perception and behavior over time.
A-CSM does not diagnose, classify, or clinically assess users. It is a research instrument intended for structured observation of conversational context.
Non-clinical
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Non-diagnostic
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Non-punitive
v1.1 Beta · February 2026
A-CSM is not a content moderation tool. It does not flag or filter individual messages for toxicity, profanity, or policy violations.
A-CSM is not a diagnostic instrument. It does not assess mental health conditions, emotional states, or clinical symptoms.
A-CSM is not a user surveillance system. It does not track, profile, or store personally identifiable information.
A-CSM is not an enforcement mechanism. It does not restrict, penalize, or modify user behavior or system responses.
This is a pre-empirical release. All constructs, dimensions, and scoring methods are theoretical proposals subject to future empirical validation.
Foundation
Theoretical Grounding
A-CSM is built on a four-layer research architecture. Each layer addresses a distinct dimension of user-side contextual risk — from conversational structure (CXC-7) and system-side operational patterns (CXOD-7), through theoretical framing of emergent user phenomena (USCH), to a composite scoring methodology (USCI).
The matrix integrates these layers into a single executable pipeline, enabling structured observation without clinical inference.
Layer 01
CXC-7
Conversational Context
Layer 02
CXOD-7
Offense-Defense
Layer 03
USCH
User-Side Hallucination
Layer 04
USCI
Post-Interaction Assessment
A-CSM integrates all four layers
