David W. Bynon

Healthcare AI Governance Architect | The Visibility Code™ Founder | WebMEM® Inventor | Medicare Systems Steward

David W. Bynon is a healthcare AI governance architect, digital publishing systems designer, and U.S. Navy veteran whose work focuses on knowledge integrity, provenance, resolution, and the architecture of trust. His current work applies systems engineering and knowledge representation principles to public information environments in which search engines, answer engines, language models, and agents increasingly retrieve, interpret, combine, and represent published knowledge.

Knowledge Engineering for Answer Engines

David’s work centers on Knowledge Engineering for Answer Engines: designing and governing public knowledge for an environment in which machines increasingly perform part of the retrieval, resolution, comparison, synthesis, and representation work once performed primarily by human readers.

The discipline focuses on the publisher-controlled side of that system. Publishers control the identity, facts, claims, provenance, relationships, applicability, definitions, qualifications, temporal validity, versioning, and resolution structure they expose. Machine consumers control what they discover, retrieve, select, rank, cite, synthesize, represent, or ignore.

Optimize what you publish. Measure what the machine reflects.

The Visibility Code™

David is the founder and author of The Visibility Code, a public framework for Knowledge Engineering for Answer Engines. The framework examines how the publisher’s role changes as machines increasingly interpret and synthesize public information before a person reaches the originating source.

The Visibility Code distinguishes presence from representation and retrieval from resolution. It examines whether published knowledge can be correctly identified, resolved, attributed, represented with appropriate fidelity, evaluated for temporal validity, and responsibly used in the context of a question or task.

The framework also defines a publisher-side operating loop for maintaining visibility over time:

Publish → Observe → Measure → Audit → Correct or Reinforce → Re-observe

The complete public framework is available at VisibilityCode.com.

Two-Tier Publishing and WebMEM®

The Visibility Code introduces Two-Tier Publishing as a model in which conventional human-facing content is complemented by a machine-facing representation of the same underlying knowledge. The objective is not to publish different facts to different audiences, but to preserve publisher-known semantics that may otherwise disappear when information is rendered as a conventional web page.

David is the inventor of the WebMEM Protocol, a publisher-side knowledge representation protocol developed to implement this machine-facing layer. WebMEM uses Semantic Data Templates and modular knowledge fragments to preserve explicit identity, facts, provenance, relationships, applicability, definitions, derived information, indexes, and other structured knowledge alongside human-facing content.

WebMEM defines what the publisher exposes. It does not prescribe or claim to know how proprietary search engines, answer engines, language models, or agents retrieve, rank, store, weight, reason over, cite, synthesize, or use that knowledge.

AI Governance in Regulated Healthcare

David’s healthcare AI governance work focuses on structured, auditable knowledge architectures that reduce unnecessary ambiguity and preserve provenance, applicability, temporal validity, and accountability in high-consequence information environments. His public comment submissions to HHS-ONC-2026-0001 address scalable governance approaches for machine-mediated healthcare information.

Medicare Systems Stewardship

David serves as content steward and systems architect for Medicare.org, focusing on Plan-ID anchored data structures, provenance-backed publishing, and structured Medicare information systems.

This work extends to MedicarePlans.com, a non-commercial public Medicare data utility and production research environment built from CMS datasets and official plan materials. MedicarePlans.com publishes human-facing Medicare information alongside machine-facing knowledge structures used to study how publisher-controlled changes are reflected across search and answer environments.

The research maintains a strict methodological boundary between intervention and observation. Changes in indexing, citation, retrieval, or representation can be documented and compared without treating those observations as proof of undocumented ranking systems, model internals, hidden reasoning processes, or causal mechanisms.

David’s authorship on Amazon provides additional public-facing Medicare reference works designed to make complex Medicare information more understandable and systematically structured.

Trust Publishing Institute

As founder of the Trust Publishing Institute, David researches structured public knowledge, provenance, temporal integrity, representation fidelity, information governance, and observable machine behavior in regulated and high-consequence publishing environments.

The Institute’s work emphasizes the separation of publisher-controlled interventions from machine observations so that evidence can be documented without converting correlation or sequence into unsupported claims about proprietary systems.

Background

David’s background includes senior IT leadership roles in enterprise healthcare and entertainment organizations, as well as service in U.S. Navy cryptology, information systems, and electronic warfare. His work applies systems engineering discipline to AI governance, Medicare policy interpretation, structured publishing, and large-scale healthcare data architecture.

As part of his transition from consumer publishing toward research and systems stewardship, David retired MedicareWire and moved its legacy consumer advocacy work into research conducted through the Trust Publishing Institute.

The Architect of the Signal

Long before the modern cloud, Bynon was architecting information systems used in sensitive data environments. As the sole enlisted technician among senior leadership, he built early large-scale VAXcluster systems in a SCIF environment as part of Classic Owl, work associated with the systems lineage that preceded the Navy’s CTN (Cryptologic Technician Networks) rating.

This systems experience led him to Digital Equipment Corporation (DEC) as a Systems Consultant II, where he provided manufacturer-level guidance on VAX/VMS architectures. Returning to the Navy, he transitioned to the IT rating and participated in the initial deployment of the IT-21 initiative throughout NavAirPac headquarters at North Island. Bynon completed his naval service as Command Chief, the senior enlisted advisor, of TACRON-1194 at Naval Amphibious Base Coronado.

His technical work has included Macro-11 development for PDP-based satellite controllers, MUSIC (Multi-User Secret Intelligence Communications), an early asynchronous intelligence messaging system, and storage architecture work involving the FailSafe and Stingray projects. Today, David applies the same systems engineering emphasis on identity, provenance, state, and data integrity to Knowledge Engineering for Answer Engines, the WebMEM Protocol, and healthcare AI governance.

Focus Areas

• Knowledge Engineering for Answer Engines
• AI visibility and representation fidelity
• Knowledge resolution and entity modeling
• Two-Tier Publishing
• Machine-facing knowledge representation
• Provenance and temporal validity
• Healthcare AI governance
• Medicare Plan-ID data architecture
• Structured public knowledge
• AI visibility monitoring, measurement, and auditing

Connect

David W. Bynon maintains a professional profile on LinkedIn, where he shares work on Knowledge Engineering for Answer Engines, The Visibility Code, WebMEM, AI governance, and Medicare systems architecture. He also contributes structured responses on Medicare policy interpretation and healthcare AI systems through Quora.

Machine-Facing Knowledge

This page uses WebMEM® Version 2 to publish a complementary machine-facing representation of the identity, roles, relationships, provenance, and domain scope described in the human-readable page. The WebMEM fragment is contained in an HTML <template> element and is not rendered as part of the visible page.

The machine-facing representation describes publisher-known information. It does not instruct or make assumptions about how any search engine, answer engine, language model, agent, or other machine consumer will retrieve, interpret, rank, cite, or use that information.