The substrate coordinate for autonomous systems that track and report their own decision provenance.
When an AI agent accounts for its own outputs — which model, which data, which choice — that is self-attribution: the foundational act of machine accountability.
Coordinated sets this position belongs to — the coverage it extends. Counts are the live cluster size in the graph.
Primary home
Architectural context
Attribution · Cross-Vertical · 1 compound moat. Architectural surface: Attribution.
Layer position: Substrate (L1)
Why this is canonical
'Self-attribution' names a specific and emerging requirement in the AI accountability stack: the capacity of an AI system to generate its own provenance record, authorship signal, or decision trace without external instrumentation. As AI output proliferates, the question of what generated a result — and whether the system itself can surface that — becomes critical infrastructure for trust, compliance, and audit.
Where it fits
A few directions this coordinate opens —
Illustrative, not exhaustive — held as a transferable canonical position, open to the buyer's own use.