Human accountability
AI assists people; it does not replace accountable approval where a decision, controlled output or regulated outcome requires human judgement.
AI / ASSISTANCE / ACCOUNTABILITY
DigiPixelOS uses AI to accelerate useful work while preserving system boundaries, provenance, review and accountable human decisions.
AI PRINCIPLES
AI assists people; it does not replace accountable approval where a decision, controlled output or regulated outcome requires human judgement.
Each DigiPixel system owns its own AI use cases, requests, outputs, reviews and usage. There is no unrestricted central AI database containing every product's operational data.
AI-assisted work should preserve useful provenance such as model configuration, prompt or brief snapshot, output context and named review where appropriate.
AI should receive only the information needed for the approved use case and respect the system, organisation and permission boundaries already in force.
AI-generated or AI-assisted material that becomes controlled, regulated or externally consequential must pass the appropriate human or compliance review.
Material AI use should support audit hooks, failure classification and sufficient operational traceability to understand how assistance was produced and reviewed.
HARD BOUNDARIES
These controls reflect the current DigiPixelOS architecture policy. They are deliberate product boundaries, not optional interface guidance.
DATA ARCHITECTURE
AI is shared as a platform capability, but operational ownership stays with the specialist system. Each system stores its own AI requests, outputs, reviews and usage in its own operational boundary.
See the governance model →This page is a public summary of DigiPixelOS product design principles. It describes intended system behaviour and governance boundaries; it is not a certification, legal opinion or claim that using software alone makes an organisation compliant.