Real-World AI Governance Center
Responsible AI for High-Stakes Human Systems
AI is already making and shaping decisions inside child welfare, public benefits, casework, and other systems where the people affected have the least power to contest an error. This Center treats that as a governance problem: not "is the model good?" but "does this whole sociotechnical system — model, people, records, pressures — catch its errors faster than it spreads them?"
Five sections, one discipline: concepts in the Field Guide, documented histories in the Domain Atlas, levers in the Practice Library, stress testing in the PAN Lab, and every empirical claim ledgered in the Evidence Registry.
The Center
Six ways in, one discipline: nothing claimed without support.
Field Guide
Understand“What do I need to understand?”
Concepts and mental models: sociotechnical systems, the robustness gap, error propagation, and governing under deployment pressure.
Domain Atlas
Domains“How does this show up in specific domains?”
How AI governance shows up in social services and other high-stakes human systems — with documented real-world case files.
Practice Library
Practice“What can institutions do?”
Actionable governance patterns and institutional controls: what each changes, who can pull it, and what can backfire.
PAN Lab
PAN Lab“What happens if we test this idea under pressure?”
Scenario-based governance stress testing: explore how governance choices reshape error-flow pathways under institutional pressure.
Evidence Registry
Evidence“What supports this claim?”
Every empirical claim on this site, mapped to its sources — academic references and real-world grounding documentation.
Work With 1023AI
Engage“How can we work with 1023AI?”
Advisory and fractional leadership, governance diagnosis, scenario-based stress-testing engagements, talks, and research collaboration.
New here? Start with the Center orientation →
Three ways in
New to the topic
Field Guide → a case file → the PAN Lab
Get the concepts, see one documented history, then watch governance choices move a stylized system.
Leading a deployment
Domain Atlas → Practice Library → Work With 1023AI
Find your domain and its failure modes, shortlist the levers, then talk about your system's actual shape.
Checking our homework
Evidence Registry → Governance Watch
Start from the sources and work backwards. Every empirical claim is ledgered; pending citations are tracked openly.
The evidence discipline, in short
Every empirical claim on this site is a ledger entry mapped to sources synced from the PAN reference library — never added by hand. Conceptual framing is labeled as framing; scenario results are labeled as illustrative PAN-run results, direction and shape only; and statements still awaiting a source carry a visible "citation pending" badge rather than quiet confidence.
The full registry — including what's pending — is public at Evidence Registry.