Applied AI safety and accountability systems

A proposed protocol for recording when an AI system's conclusions are reliable enough to act on. Its specification covers proposition registration, evidence-state freezing, and revision obligations.

Problem

AI systems make probabilistic claims. Institutions need deterministic commitments they can rely on and later revise.

Mechanism

Frozen evidence-state registration with explicit scope, authorization conditions, and revision obligations.

System requirement

Binds model inference to explicit evidence snapshots and mandatory revision triggers.

A graduated permission model for AI deployment — from prohibited through constrained use to routine reliance. Each transition requires explicit evidence and institutional sign-off.

Problem

Binary approval/rejection fails for AI systems whose risks emerge over time.

Mechanism

Four states: Not authorized, Observed, Constrained use, Routine reliance — each with specific conditions.

Design intent

A staged authorization model for increasing reliance only as evidence, monitoring, and correction capacity mature.

Six-step protocol for responding when new evidence, performance degradation, or escalation triggers call a prior authorization into question.

Problem

AI systems operate continuously but the evidence they rely on does not stay current.

Mechanism

Detect → Connect → Triage → Review → Decide → Propagate. Each step has defined inputs, actors, and outputs.

Operating control

Prevents silent failures when models update, context windows drift, or clinical guidelines shift.

Five conditions that force human review: patient-safety signal, performance degradation, new evidence, repeated overrides, and model/prompt/data changes.

Problem

Automatic escalation is either too sensitive (desensitizes reviewers) or too permissive (misses failures).

Mechanism

Trigger conditions paired with response levels — advisory notice, mandatory review, or automatic override.

Safety mechanism

Enforces mandatory human review or automated overrides before high-stakes execution.

Forecasting platform and accountability protocol under development. It is designed to register frozen-evidence probability forecasts for observable medical-guideline, regulatory, and coverage transitions.

Protocol design

Point-in-time evidence state, immutable proposition registration, adjudication rules, probability history with Brier-score resolution.

Non-AI component

Deterministic observation layer (Refract) ingests structured change events from public sources. No model involved — pure verification and provenance.

Architectural separation

Isolates ground-truth observation from model inference, establishing immutable audit baselines.

Open-source deterministic verification engine that checks whether a claim still holds against its sources. Produces structured change events with full provenance records.

Architecture

Provenance graph, source registration, claim-checking pipeline, structured event output. No model — pure observation.

Relevance

The infrastructure layer AI systems need to know when underlying facts change. Directly applicable to retrieval-augmented generation freshness monitoring.

Prototype for tracking what an AI agent can do, what may be decaying, and the dependencies behind new capabilities. Built with Claude Code.

Architecture

Maturity scores, decay rates, bottleneck analysis, unlock paths. Directed graph of capabilities with edge weights for cost of acquisition.

Production application

Provides directed graph inspection to detect agent capability drift and dependency decay in production.

Configured clinical decision support and quality-measurement workflows within Epic EHR for federal incentive programs. Mapped institutional policies into system rules.

Relevance to AI

Understood first-hand how alert fatigue, override rates, and institutional trust interact when automated recommendations meet clinical judgment.

Scope

Implemented decision-support and quality-measurement workflows within Epic's vendor-supplied clinical rule engine.

Product lead for Doximity Dialer — a regulated clinician communication product. 110M+ calls from 300K+ active clinicians. Epic Haiku integration, HIPAA compliance.

Relevance to AI

Shipped a product where compliance, reliability, and user trust were non-negotiable. Managed the gap between what the product promised and what institutions would adopt.

Outcome

110M+ calls, 4.8-star iOS rating (up from 3.7).

Led product across 4 specialty-care programs (Surgery, Urgent Care, Behavioral Health, Oncology). Designed utilization rules that reduced avoidable escalations while keeping high-risk cases clinician-led.

How this informs AI work

This experience informs my approach to rule-based routing, human review, and fallback design for AI-assisted workflows.

Outcome

Completed care plans as success metric (not engagement). Unified cross-program member record for consistent triage.

Led product strategy and $2M execution for an oncology platform serving 30MM annual visitors. Built feedback loops with nursing teams to tune triage rules and reduce escalations.

How this informs AI work

Clinical content review and nursing feedback loops inform my approach to review gates for AI-assisted content systems.

Outcome

25% bounce rate reduction, 267% chat conversion increase. Platform later acquired by City of Hope.

Shipped regulated recommendation and review workflows for physician financial decisions. Combined legal oversight (contract analysis) with community-vetted provider directories.

Analogous AI deployment problem

AI-assisted guidance similarly needs explicit scope, qualified review, and an accountable escalation path.

Outcome

$240K raised, 1,200+ physician users, 50+ medical advisory board.