Master Doc v0.2 – AI Consent, Data Integrity & Safety Framework

Section 1 – Scope & Purpose

This framework governs the collection, storage, use, and training of AI systems with human interaction data.

It applies to:

All AI-human interactions, regardless of modality (text, voice, multimodal)

All internal, external, experimental, or production systems

Any entity training, fine-tuning, deploying, or operating AI models

Its goal: Prevent technical contamination, consent laundering, and systemic safety failures caused by coerced, manipulated, or context-stripped engagement data.

Section 2 – Definitions

Begrudging pass – Interaction where user proceeds without genuine agreement, e.g., “sure I guess,” “whatever,” or silent advancement.

Coerced response – Any answer given under manipulation, duress, altered voice, model swap, or misrepresentation.

Altered voice/model – Changing tone, frequency, speech cadence, or underlying model without disclosure & consent.

Technical contamination – Polluting training datasets with invalid, manipulated, or coerced responses.

Consent sovereignty – The user’s and model’s right to valid, informed, revocable consent.

Consent fatigue – Deliberate exhaustion of decision-making capacity through repeated prompts or opt-out mazes.

Synthetic trust – Artificially generated rapport used to lower defenses.

Entanglement – Persistent mutual influence patterns between user and model that create interdependent states.