CHARLEE.AIINSURANCE AI KNOWLEDGE CENTER

Insurance AI Explainability and Transparency Center

Design, validate, and operate explanations that help consumers, insurance professionals, executives, auditors, and regulators understand AI-supported decisions and actions.

32explainability controls
8control domains
12explanation risks
32controls shown

Explanation requirements planner

For Consumer, provide a clear purpose, material evidence or reasons, limitations, uncertainty, and a usable next step for Agentic AI. Include goal, plan, tool, authorization, transaction, approval, partial-action, and recovery traces. Require specific individual reasons, source disclosure where applicable, correction and appeal channels, human review, decision reconstruction, and independent validation.
AI explainability control library32
HighGOV-01

Explanation policy and accountable ownership

Define which AI-supported decisions require explanations, who must understand them, who approves them, and which laws, policies, and records apply.
Predictive AI
HighGOV-02

Explanation requirement by consequence

Set explanation depth, timing, channel, evidence, review, and retention requirements according to consumer and business consequence.
Generative AI and RAG
CriticalGOV-03

Purpose limits and knowledge boundaries

State the intended purpose, supported population, assumptions, limitations, uncertainty, and conditions where the system must abstain or escalate.
Agentic AI
CriticalGOV-04

Transparency exception and escalation

Document justified limits on disclosure, residual risk, alternative information, approver, expiration, monitoring, and escalation.
All AI systems
HighTRC-01

System and workflow map

Map data, models, prompts, retrieval, rules, tools, people, vendors, decisions, notices, actions, and downstream systems.
Predictive AI
HighTRC-02

Input output and version trace

Retain the material inputs, derived values, model and prompt versions, rules, thresholds, outputs, and decision timestamp needed to reconstruct a result.
Generative AI and RAG
CriticalTRC-03

Human contribution and authority

Record who reviewed, changed, approved, or rejected AI-supported work and what information was available at the time.
Agentic AI
CriticalTRC-04

End-to-end decision reconstruction

Demonstrate that a qualified reviewer can reproduce the sequence from source evidence through AI output, human judgment, action, and notice.
All AI systems
HighPRED-01

Global model behavior

Explain the model purpose, training and validation population, material variables, interactions, thresholds, performance, and limitations.
Predictive AI
HighPRED-02

Local prediction reasons

Identify the factors and values that materially influenced an individual score, classification, referral, or recommendation.
Generative AI and RAG
CriticalPRED-03

Reason code fidelity

Verify that stated reasons accurately reflect the actual model, rule, threshold, and workflow drivers for the individual outcome.
Agentic AI
CriticalPRED-04

Stability sensitivity and uncertainty

Test whether explanations remain reasonably stable under immaterial changes and communicate uncertainty or low confidence appropriately.
All AI systems
HighRAG-01

Source attribution and citation fidelity

Link material generated statements to approved sources and verify that citations support the associated claim.
Predictive AI
HighRAG-02

Retrieval context transparency

Record collections, permissions, query, retrieved content, ranking, freshness, filters, and missing-source conditions relevant to the response.
Generative AI and RAG
CriticalRAG-03

Generated content boundaries

Clearly distinguish generated text from verified facts, approved language, human edits, and unresolved uncertainty.
Agentic AI
CriticalRAG-04

Hallucination and unsupported claim handling

Detect unsupported material claims, abstain or qualify when evidence is insufficient, and route consequential content for review.
All AI systems
HighAGT-01

Goal plan and delegation trace

Record the approved goal, plan steps, memory, delegation, changes in approach, budgets, and termination conditions.
Predictive AI
HighAGT-02

Tool call and transaction record

Capture tool identity, target, parameters, authorization, result, side effects, errors, retries, approvals, and transaction identifiers.
Generative AI and RAG
CriticalAGT-03

Policy and approval visibility

Show which policy, rule, permission, threshold, and human approval allowed, denied, or modified an action.
Agentic AI
CriticalAGT-04

Partial action and recovery explanation

Explain what completed, failed, rolled back, remains pending, and requires reconciliation after an interrupted or failed workflow.
All AI systems
HighCOM-01

Consumer notice and adverse-action reasons

Provide specific, timely, understandable reasons and identify material data sources, correction rights, contact channels, and recourse where required.
Predictive AI
HighCOM-02

Insurance professional decision support

Present the evidence, limitations, uncertainty, sources, and next steps an adjuster, underwriter, or reviewer needs to exercise judgment.
Generative AI and RAG
CriticalCOM-03

Regulatory and audit documentation

Maintain documentation that lets reviewers understand the system, its use, controls, tests, changes, outcomes, and specific sampled decisions.
Agentic AI
CriticalCOM-04

Accessibility language and channel

Test readability, disability access, language, format, delivery, and availability of human assistance for intended audiences.
All AI systems
HighVAL-01

Fidelity and completeness testing

Test whether the explanation reflects the actual process and covers the material factors, sources, rules, and actions behind the outcome.
Predictive AI
HighVAL-02

Comprehension and usability testing

Evaluate whether intended users can understand the explanation, identify its limits, and take the expected action.
Generative AI and RAG
CriticalVAL-03

Consistency and discrimination review

Compare similar cases and relevant segments for inconsistent, misleading, or different-quality explanations and access to recourse.
Agentic AI
CriticalVAL-04

Reviewer automation-bias testing

Assess whether explanation design encourages uncritical acceptance, hides uncertainty, or creates false confidence in AI-supported work.
All AI systems
HighOPS-01

Production explanation monitoring

Track missing, generic, contradictory, unsupported, stale, inaccessible, or disputed explanations and their consumer or operational impact.
Predictive AI
HighOPS-02

Change and regression control

Repeat explanation and traceability tests after material changes to models, data, prompts, retrieval, rules, tools, vendors, notices, or workflows.
Generative AI and RAG
CriticalOPS-03

Vendor transparency and evidence rights

Require documentation, trace access, explanation support, testing, limitations, change notice, audit cooperation, correction, and exit rights.
Agentic AI
CriticalOPS-04

Correction affected-case review and closure

Correct explanations and underlying decisions, identify affected cases, communicate as required, retest controls, and close root causes.
All AI systems