Balancing AI Efficiency and Liability Risk in Canadian Business

AI and Automation Liability: What Insurance Pros Need to Watch Now

Canadian businesses are moving fast on AI and automation to gain efficiency and margins. What used to be sci-fi is now standard practice across sectors.

• Customer support: chatbots that answer questions and troubleshoot issues
• Manufacturing: designing improved versions of existing products
• Transportation: predictive maintenance schedules
• Healthcare: medical imaging analysis and triage
• Agriculture: automated crop harvesting

Progress brings exposure. The legal ground under AI is still shifting, and the risk is landing on the balance sheets of insureds-and, by extension, their insurers.

Key liability concerns that affect coverage and claims

• Regulatory ambiguity: Canada and the US lack consistent AI rules, while the EU has moved ahead with its AI Act. Cross-border inconsistencies make fault attribution and subrogation harder.
• Due diligence is harder to prove: Self-learning systems challenge "foreseeability." Defendants may struggle to show they took reasonable precautions as models update and adapt.
• Complex attribution of fault: Developer, software provider, integrator, deployer-or all of the above? Multi-party disputes can extend timelines and costs.
• Higher litigation likelihood: With few precedents and opaque decision logic, plaintiffs may have an easier time alleging negligence, misrepresentation, or product defects.

Case study: Air Canada's chatbot misadvice

A small-claims ruling found Air Canada liable after its chatbot gave bad information about a rebate and the airline refused to honour it. The company argued the chatbot was a separate legal entity. The court disagreed and ordered reimbursement.

Takeaway: you own what your AI tells customers. Disclaimers won't shield you if the system is positioned as an official channel.

Risk controls to reduce loss frequency and severity

• Fact-check outputs: Train staff to verify AI responses. Add human review for customer-facing answers and high-impact decisions to reduce "hallucination."
• Use cases with guardrails: Keep AI in analysis, prediction, and task automation. Do not rely on it for legal, financial, or health advice without licensed professional oversight.
• Monitor and retrain: Continuously test chatbots and decision systems; log prompts, responses, and model versions for auditability.
• Document the lifecycle: Keep records of design choices, data sources, testing, approvals, and change management. Transparency helps demonstrate due care.
• Vendor management: Require warranties, security controls, incident notification, and indemnities from AI providers and integrators.
• Incident playbooks: Define takedown, rollback, and customer-communication steps for AI errors or model drift.

Coverage and wording considerations for insurers

• Map exposures to policies: Tech E&O/professional liability (bad outputs), product liability (embedded AI in devices), cyber (data, outages, ransomware), media liability (defamation), CGL (bodily injury/property damage), D&O (governance failures).
• Clarify definitions: Define "automated decision system," "model," and "training data." Address learning updates, third-party models, and AI-as-a-service.
• Underwriting questions: Use cases, human-in-the-loop controls, data provenance, evaluation/QA cadence, logging, red-teaming, and vendor contracts.
• Loss control: Recommend model inventories, access controls, performance thresholds, and kill-switches for customer-facing AI.

What brokers and risk managers can do right now

• Ask clients to inventory all AI systems and map each to business impact and owners.
• Push for written policies on testing, approval, monitoring, and customer communications.
• Review contracts with AI vendors for liability caps, IP, indemnification, and logs.
• Validate coverage fit and gaps across E&O, product, cyber, and CGL. Align limits with modeled worst-case events.

Bottom line

AI and automation can improve efficiency, but liability exposure is real, especially without clear regulations. Strong governance, precise documentation, and disciplined deployment reduce the chance of a claim-and strengthen the defense if one lands.

For more information: Please contact us at gcs.ca@aviva.com.

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