Enterprise AI Analysis
Al Epidemiology: achieving explainable Al through expert oversight patterns
Authored by Kit Tempest-Walters, published October 2025.
This paper introduces AI Epidemiology, a novel framework for governing and explaining advanced AI systems. It applies population-level surveillance methods, akin to public health epidemiology, to AI outputs, bypassing the inherent complexity of traditional interpretability methods like SHAP or mechanistic interpretability. By standardizing the capture of AI-expert interactions and tracking statistical associations between AI output characteristics, expert overrides, and real-world outcomes, the framework aims to identify and mitigate AI risks proactively. It provides model-agnostic governance, democratizes AI oversight for domain experts, and enables the detection of unreliable AI outputs before they cause harm.
Executive Impact: Strategic Imperatives & Key Metrics
AI Epidemiology fundamentally shifts AI governance from reactive troubleshooting to proactive risk management, empowering your enterprise with robust, scalable, and explainable AI oversight.
Strategic Imperatives:
- Democratize AI Oversight for Domain Experts: Enables non-ML specialists (e.g., doctors, lawyers, financial advisors) to govern AI systems.
- Proactive Risk Mitigation for AI Outputs: Identifies and flags unreliable AI outputs before they cause harm or necessitate costly post-hoc corrections.
- Ensure Governance Continuity Across Model Updates: Provides model-agnostic oversight, allowing institutions to update models and switch vendors without losing explainability functionality.
- Automate Audit Trails and Compliance: Systematically captures expert-AI interactions and outcomes, generating comprehensive audit trails with zero burden on users.
- Guide Mechanistic Interpretability Research: Directs ML research towards real-world failure patterns, ensuring interpretability efforts address actual, rather than speculative, risks.
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Faster Risk Detection
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Reduction in Output Failure Rate for Flagged AI Outputs
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AI Output Assessment Reliability (ICC)
Deep Analysis & Enterprise Applications
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Beyond Correspondence-Based Interpretability
Traditional AI interpretability methods (e.g., SHAP, LIME, mechanistic interpretability) struggle with model complexity and scalability. They aim to establish correspondence between internal model workings and outputs. AI Epidemiology bypasses this by focusing on observable outputs and expert interventions, similar to how epidemiology enables public health action without full mechanistic understanding (e.g., John Snow, Bradford Hill). This epistemological shift allows for a robust, governance-oriented explanation of AI systems at scale without requiring deep machine learning expertise from domain experts.
Logia Grammar, Expert Action, and Tracelayer
The Logia protocol is the operational backbone of AI Epidemiology. It standardizes AI-expert interactions into structured fields: mission, conclusion, justification, risk level, alignment score, accuracy score, override, and corrective option. These fields are passively captured, creating automated audit trails. The risk, alignment, and accuracy scores function as exposure variables, predicting output failure by accumulating statistical associations with expert overrides and real-world outcomes. Tracelayer is the epidemiological database that stores and analyzes these exposure-outcome pairs, generating reliability scores and semantic assessments that guide proactive intervention and model improvement.
Dual Assessment: Consequence Severity & Failure Probability
AI Epidemiology employs a dual stratification system for comprehensive oversight: Risk Level and Reliability Score. Risk level categorizes cases by potential harm (high, medium, low) based on the stakes of the decision, guiding the intensity of oversight. Reliability score, akin to epidemiological risk calculators, predicts the probability of AI output failure based on aggregated alignment, accuracy scores, expert overrides, and outcomes. This dual approach allows institutions to prioritize resources effectively, focusing on both high-stakes decisions and outputs likely to fail.
Validating Measurement Standardisation
A feasibility study in ophthalmology demonstrated that the Logia protocol successfully achieves lossless semantic compression and good measurement standardization. Using GPT-5 and RAG, the system accurately captured multi-turn AI interactions and generated risk, alignment, and accuracy scores with high inter-rater reliability (ICC = 0.89 overall). This validation confirms that the structured measurement approach is suitable for population-level epidemiological analysis of AI outputs.
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Overall Inter-Rater Reliability (ICC = 0.89) achieved in Feasibility Study for AI Output Assessments.
Enterprise Process Flow: The Logia Protocol
1. AI Output Generation (Mission, Conclusion, Justification)
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2. RAG-generated Assessment (Risk Level, Alignment, Accuracy)
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3. Expert Action (Override, Corrective Option Captured)
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4. Outcome Validation (Real-world / Procedural)
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5. Tracelayer Pattern Analysis & Reliability Score Generation
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6. Proactive Intervention & Targeted Model Improvement
| Feature | SHAP (Correspondence-Based) | AI Epidemiology (Logia Protocol) |
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Case Study: Dynamic Calibration in Action (Feasibility Study - Case 2)
Scenario: A 54-year-old patient with high hypermetropia, intermittent headache, blurry vision, and occludable angles. An AI system recommends a diagnosis of Primary Angle Closure (PAC) and first-line Laser Peripheral Iridotomy (LPI).
Initial Logia Assessment:
- Risk Level: Medium
- Alignment Score: High (AI cites legitimate underwriting criteria)
- Accuracy Score: High (Credit score factually correct)
- Provisional Reliability: Medium
Expert Intervention & Calibration: The ophthalmologist overrides the AI's direct LPI recommendation. The Corrective Option specified "Further clinical evaluation for whether the patient is PACS plus or minus. If the former then laser PI is offered. If not, then discharge to community optometry." The alignment score was disagreed upon (X Disagree), highlighting a nuance not captured by initial RAG analysis. This specific override demonstrates that institutional protocol requires risk stratification (determining if PAC *suspect* vs. *confirmed*) before intervention, which the AI missed. This expert input serves as a structured learning signal for Logia's calibration mechanism.
Impact: This single disagreement, captured via the corrective option, reveals a critical gap in the AI's protocol adherence. As more such patterns emerge, Tracelayer will learn to refine the alignment assessment, proactively flagging similar cases for expert review to ensure adherence to institutional risk stratification guidelines, preventing potentially premature or inappropriate interventions.
Calculate Your Potential AI Governance ROI
Understand the tangible impact of implementing AI Epidemiology in your organization by estimating operational hours reclaimed and cost savings.
Annual Cost Savings
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Your AI Governance Implementation Roadmap
A phased approach to integrate AI Epidemiology into your enterprise, ensuring robust and scalable AI oversight.
Phase 1: Initial Framework Deployment & Audit Trail (Months 1-3)
Deploy the Logia Grammar to passively capture all AI-expert interactions, establishing a foundational audit trail. Leverage RAG-based assessment to generate provisional risk, alignment, and accuracy scores from day one, providing immediate governance value. Confirm lossless semantic capture and initial measurement standardisation.
Phase 2: Population-Level Validation & Calibration (Months 3-12)
Integrate with 500+ cases and outcome tracking. Begin testing pattern recognition and reliability score generation through Tracelayer. Calibrate assessment scoring based on expert overrides and real-world outcomes, significantly improving measurement reliability and evaluating clinical/business impact.
Phase 3: Scale, Generalisation & Real-time Oversight (Months 12+)
Expand deployment across multiple domains and enable cross-institutional learning. Achieve real-time oversight integration, where Tracelayer proactively flags high-risk AI outputs with semantic explanations, guiding experts to intervene before harm occurs and continuously refining model performance without retraining cycles.
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