AI's mistakes and hallucinated content are causing many issues around responsibility. Because of this, we'll see tougher checks at every stage of AI output. When I built an AI rule system for legal process, I learned that carefully checking facts manually and confirming sources was essential to defending AI use to clients, courts, and regulators. I think insurance companies will want to see these kinds of audit trails and standards before they provide coverage, especially where AI mistakes could lead to wavier of privilege or open up an attorney to malpractice claims. To prepare, law firms and others will need to implement structured AI governance to get clarity on internal risk tolerance and AI tool management.
I've built SaaS platforms, and I see what insurers worry about with AI. They're scared of hallucinations or unsupported claims leading to big mistakes. I saw this firsthand when AI guidance broke a customer's workflow. Suddenly you can't tell if it's the platform's fault or the user's error. Until providers build systems that show their work, liability just floats between the model creator and the customer.
Our health-tech platform sometimes gets a little ahead of itself predicting conditions, and insurers react differently. One carrier flagged it as a "hallucination" claim, which is specific. Another just tossed it under generic cyber risk. That creates a mess. You have to push for clearer policy language. From what we've seen, regular model checks and good records make underwriting a lot smoother.
Briefly: Insurers and the Legal Community are reacting to the emergence of new risks associated with Generative AI (a.k.a. GenAI) and the impacts of those reactions are evident now in how they shape Engineering, Contracts and Allocation of Risk. What Factors Are Driving Underwriting Activity? The main factors driving underwriting activity revolve around Claims associated with Hallucinations (i.e., false or plausible Claims), Defamation/IP Infringement, Biased/Discriminatory Outputs and Automated Decisions resulting in the loss of Financial and/or Reputational Value. Each of these types of Claims is dissimilar to "Classic" Cyber Claims; rather, "Cyber" typically involves the Loss of Data or Unauthorized Access to Data; while, with the Claims associated with AI, the focus is on What Comes Out of the Model, but is, in essence, Incorrect, Harmful and/or Unexplainable. Insurers have begun to price both types of Claims; however, most Insurers are also beginning to exclude AI-Coverage from their Policies because of the distinctiveness of the Loss Modes and Measurement Issues associated with these types of Claims. How will this affect the Engineering Lifecycle? As new and changed Engineering Requirements (e.g., Immutable Audit Trails, Documented Rigorous Input/Output Provisions, Deterministic "Liability Modes" (guardrails and monitoring for outputs that are deemed to be "high-risk" and/or the use of Standardized Validation Suites and Model Cards)) are being established by Insurers increasingly as a Coverage Requirement, the Engineering requirement to test, monitor and document Incident "Playbooks" will increasingly require evidence of Demonstrable Testing, Monitoring and Incident "Playbooks" (i.e., Auditable Evidence) that reasonable measures were taken to Mitigate Risk. How will we see Liability Standardized in 2-3 Years? Not entirely. I believe that different Layers of Liability Standards will emerge, including Contractually Allocated liabiilty between Model Providers and Model Users, as well as Standards and Certification Programs for Markets that Insurers will rely upon. Courts and Arbitrators will create precedent for future Developments, but the "Black Box" with respect to AI Models will inhibit Pure Standardization for the Immediate Future. However, Practical Development will take place in the form of Enforceable SLAs, Provenance Requirements, and Industry Standards.
1) I think that there is a particularly high incidence of liability cover verses hard exclusions in this area right now because a very high proportion of claims and related risk relate to financial or operational losses: erroneous decisions (business, financial, medical) that have been generated by model output, biased credit scores, incorrect contract generation etc. Insurers are beginning to understand that the kind of risk they are underwriting with respect to AI is not at all like cyber, and that there are loss scenarios that are endemic to AI risks that are likely based on the internal logic of the model rather than some form of intrusion or attack, so policies that are tailored to these sorts of AI products are going to be needed in any event. 2) Once liability is properly defined, I also think we are likely to see model engineering incorporate features like immutable audit trails and hard guardrails into the design - at least for any applications that will be deployed where there are regulated activities or other forms of accountability around outputs. There will be pressure on providers to be able to explain outputs and internal functions of the model. 3) Liability consensus is unlikely to be reached in the near future. It will continue to be a moving target (but hopefully at least a known target) in which responsibility will be spread between providers, deployers, and users for a period of time, subject to the specific contractual arrangement and operational context. The "black box" nature of most LLMs and generative models also means that a one-size-fits-all for liability assignment is not really possible in any event.
1) From a product and operations perspective, the biggest AI risks driving insurers to bespoke policies are non-functional or service failures: hallucinations, misinformation, and other biased outputs that cause downstream errors. The underlying harms are categorically different from baseline cyber events, and policies need to be worded explicitly to cover the AI making autonomous decisions 2) Liability pressure will accelerate a more formalised and documented engineering lifecycle. Audit logging, error-tracking dashboards, and pre-deployment risk assessments will become a necessity to qualify for coverage. Embedding explainability, and more directly "liability modes" within models, will also become a precondition to qualify for coverage. 3) In the short-medium term, standardising liability coverage will be hard, as who is liable for the behavior of the outputs is highly case-specific. In the 2-3 years time frame, we might see use-case or sector-specific precedent being set, but given the complexity and opacity of LLMs, a one-policy-fits-all insurance product is likely to be unfeasible, forcing providers and enterprises to reach coverage agreements on a case-by-case basis.
1) The main issue for the industry right now is the type of potential harm AI systems may cause. In a simple language, we are facing with a 'nontraditional' types of harm that no longer fit within exsiting categories (e.g., cyber risk, professional liability etc.). The major driver for underwriting is already known hallucinations. For instance, when AI systems provide you the reference to a court case, or even a legal act (!) which has never existed in the first place. Some examples even include situations when AI provides you with a reference to a real case, but twist circumstances in such a way so that they 'fit' your answer or question you ask in the chat. Those problems usually excluded from the scope of liability, as they fall within the definition of a content generation issue, not system intrusion. One more issue is that AI systems are inheritably biased because they are created by humans. That's why certain discriminator elements could be incorporated into AI algorithms by design. Traditional cyber insurance does not contemplate liability based on discriminatory outputs, leading some carriers to exclude AI-decisioning altogether. 2) Legal side of this question already changes how algorithms operate. After recent famous cases in which ChatGpt allegedly forced a young guy to commit a suicide, they changed the way how chat must respond to conversations related to suicide and related topics. As legal risks around generative AI become more visible, we should expect the way models are engineered to change quite significantly. Developers will likely be required to keep permanent and tamper-proof records showing how a model produced certain outputs, since courts and insurers will want those details when something goes wrong. We may also see the emergence of special "liability modes," where models operate with narrower guardrails, more conservative output behavior, and stricter refusal rules. On top of that, insurers will increasingly demand clearer documentation explaining how models work, where their data comes from, and what limitations they carry. In short, legal pressure will push AI development toward systems that are more traceable, more explainable, and much more defensively designed.
Hi, 2. I anticipate engineering teams will start striving for verifiable output pipelines in which each answer can be traced back to the source data. In one deployment, after a client questioned a number that couldn't be figured out, we tacked on something we called safe output mode and it soon became a requirement for our other deployments. Insurers are also sure to favor models with hard guardrails, little creativity in high risk use cases and immutable audit logs. Explainability will shift to a condition for policy approval. Best regards, Ben Mizes CoFounder of Clever Offers URL: https://cleveroffers.com/ LinkedIn: https://www.linkedin.com/in/benmizes/