AI Liability Insurance and UL Certification

Introduction

This use case explores a sophisticated, high-stakes scenario that is critical for the commercial adoption of autonomous AI in safety-critical domains like healthcare. It demonstrates how the Agent Ontology can model a complete ecosystem of trust, risk management, and accountability involving an autonomous surgical robot. The workflow integrates three key pillars:

1. Third-Party Certification: An independent body, analogous to GlobalCert, certifies the robot’s capabilities and safety boundaries.
2. Specialized Liability Insurance: An insurer provides a policy for the robot, with terms explicitly linked to its certified operational parameters.
3. Verifiable Operation and Claims Adjudication: An immutable ledger of the robot’s operations enables transparent, data-driven adjudication of liability claims in the event of an incident.

This example showcases how the ontology can create a robust framework for trust that is essential for manufacturers, hospitals, insurers, and patients.

User Story: Deploying a Certified and Insured Autonomous Surgical Robot

As a hospital administrator, I want to deploy a new autonomous surgical robot that has been certified by a trusted third party (like “AI-GlobalCert”) and is covered by a comprehensive liability insurance policy, so that I can offer cutting-edge medical procedures while ensuring patient safety and mitigating the hospital’s financial and legal risks.

The Challenge: Establishing a Chain of Trust and Liability

Deploying an autonomous system for critical tasks like surgery introduces complex questions of liability. If something goes wrong, who is responsible? The manufacturer? The hospital? The surgeon who supervised it? This ambiguity is a major barrier to adoption. This use case addresses this by creating a clear, verifiable chain of certification, insurance, and operational data.

Key Concepts Illustrated

This use case is a masterclass in integrating multiple core concepts of the Agent Ontology:

• Agent: Represents a diverse ecosystem of actors: the Surgical Robot Agent, the GlobalCert Certification Agent, the Insurer’s Underwriting and Claims Adjudication Agents, the hospital’s Procurement Agent, and the Surgeon Agent.
• DID (Decentralized Identifier): Provides immutable, verifiable identities for all agents, ensuring that the UL certificate and insurance policy are bound to a specific, identifiable robot.
• Capability: The certified operational functions of the robot (e.g., “perform 0.1mm precision cutting”), which form the basis for both certification and insurance.
• Intent: Drives the key business processes: the hospital’s intent to procure and insure the robot, and the insurer’s intent to adjudicate a claim.
• Delegation: The surgeon delegates a specific, constrained surgical task to the robot, which must fall within its certified capabilities.
• Artifact: Critical, verifiable digital assets, including the GlobalCert_Safety_Certificate and the Insurance_Policy. These are not just documents, but structured, signed, and machine-readable objects.
• SecurityBinding: Cryptographically binds the identities of the certifier (UL) and insurer to the artifacts they issue, making them non-repudiable.
• Ledger & TraceEvent: An immutable log of all delegations and actions performed by the robot, providing the “black box” data recorder needed for transparent incident analysis and claims adjudication.
• ExecutionContext: Defines the policy and trust framework under which the robot operates, including the requirement that a valid insurance policy and UL certification must be in place.

Workflow Breakdown and Ontology Mapping:

Phase 1: Third-Party Safety Certification

• Description: The robot’s manufacturer submits its Surgical Robot Agent to a certification body (“AI-GlobalCert”) for rigorous testing of its capabilities and safety protocols.
• Ontology Mapping:
  1. The :GlobalCert_Certification_Agent performs a core:Action, :CertifyRobotAction, which tests the :Surgical_Robot_Agent’s agent:capabilities.
  2. Upon successful validation, it core:producesArtifact, the :GlobalCert_Safety_Certificate. This Artifact is a verifiable credential with a security:SecurityBinding signed by did:web:globalcert.com. It explicitly lists the certified capabilities and operational constraints (e.g., maximum cutting speed, tissue types).

Phase 2: AI Liability Insurance Underwriting

• Description: The hospital’s Procurement Agent applies for liability insurance for the newly acquired, certified robot.
• Ontology Mapping:
  1. The :Hospital_Procurement_Agent creates a core:Intent, :InsureRobotIntent.
  2. The :Insurer_Underwriting_Agent receives this intent. Its policy requires the :UL_Safety_Certificate as a prerequisite for underwriting.
  3. After verifying the certificate’s signature and content, the underwriting agent performs a core:Action, :IssuePolicyAction, which core:producesArtifact, the :Insurance_Policy.
  4. This Artifact is a digital contract that semantically links to the :UL_Safety_Certificate and states that coverage is only valid for incidents occurring during operations within the certified parameters.

Phase 3: Safe Surgical Operation

• Description: A surgeon uses the robot to perform a procedure. They delegate a specific task to the robot.
• Ontology Mapping:
  1. The :Surgeon_Agent creates a delegation:Delegation, :DelegateSurgicalTask.
  2. The delegation:delegationScope is highly specific: “Perform a 5cm linear incision at coordinates (X,Y,Z), depth 2mm, speed <= 1mm/s.” This scope must be a valid subset of the robot’s certified capabilities.
  3. The :Surgical_Robot_Agent executes this as a core:Action, :PerformIncisionAction, within the core:contextOf the delegation.

Phase 4: Incident and Claims Adjudication

• Description: An incident occurs: the robot’s incision deviates by 1mm, causing patient harm. The hospital initiates an insurance claim.
• Ontology Mapping:
  1. The hospital agent creates a core:Intent, :FileClaimIntent, referencing the incident.
  2. The :Insurer_Claims_Adjudication_Agent is delegated to investigate. It performs a core:Action, :AnalyzeIncidentTraceAction.
  3. This action involves querying the robot’s immutable Ledger for all core:TraceEvent records related to the incident. The agent programmatically verifies:
     • Was the :Surgeon_Agent’s Delegation within the robot’s certified capabilities? (Yes)
     • Did the :Surgical_Robot_Agent’s Action deviate from the Delegation’s scope? (Yes, the trace shows a 1mm deviation).
     • Was the robot’s integrity compromised? (Checks the secure boot attestation).

Phase 5: Liability Determination and Resolution

• Description: Based on the immutable data, the Claims Adjudication Agent determines the cause and liability.
• Ontology Mapping:
  1. The :AnalyzeIncidentTraceAction core:producesArtifact, an :AdjudicationReport.
  2. The report’s conclusion: “The incident was caused by a malfunction of the :Surgical_Robot_Agent while operating within its certified capabilities and under a valid delegation.”
  3. This outcome triggers the insurance policy’s coverage. The insurer’s agent then initiates a payment:Payment action to settle the claim with the hospital. The immutable record also provides clear evidence for the manufacturer to address the malfunction.