Trustworthy AI – A Market-Driven approach
Market-driven approach to achieving more dependable AI performance and guardrails through shared responsibility and liability
What if insurance companies - not regulators, governments, or tech giants - hold the key to fixing AI’s biggest reliability and accountability problems? What if a market-based solution that drives innovation, fits well with existing business practices, and saves money for all parties, were presented to you? Would you consider it?
For more than 30 years, software companies have asked users to agree to a ‘click’ license before using their products. They have become so common and so verbose that most people click “accept” without reading them. These licenses usually protect the software company from being held responsible if something goes wrong. For traditional software, this arrangement mostly worked. Software was predictable: if you entered the same information, you got the same result every time. This is called “deterministic” behavior.
AI systems are not predictable in the same way. They are “non-deterministic,” meaning the same question can produce different answers at different times. This happens because the data AI relies on to develop a response changes, grows, or is interpreted differently each time the system is queried. In that way artificial intelligence is fundamentally different. But it is being sold under the same old licensing rules.
We already know that AI can cause real harm. It can present false information that appears to be reliable. It can produce results tuned and filtered to please you rather than give a complete response. It can push an agenda embedded in the algorithm that you are not aware of. Its output can damage a company’s reputation, hurt customers, or cause financial losses in unexpected ways.
Despite this, companies using AI platforms agree to licenses that shield those AI platform companies from responsibility. Many users accept these terms without realizing how different AI really is.
As large companies begin using AI at scale, this imbalance needs to be corrected. Responsibility for AI-related harm should be shared between the companies that build AI systems and the companies that use them.
The insurance industry is well positioned to motivate both parties to make this happen through a practical, market-based approach—one that reduces risk, lowers costs, and creates new business opportunities for all of the players involved.
Here is how such a system would work.
AI developers and the companies that use their systems, and who already work under two-party contracts via the click license or customized equivalent, would negotiate and agree to an additional clause in the existing two-party contract. The modification would require regular quality checks focused on unusual or extreme situations—often called “outlier cases.” These are scenarios that may be rare but could cause serious harm if the AI responds badly to them.
If an outlier test shows that the AI produces a harmful result, both parties would be required to act. The user company would first review how it is using the AI. If fixing the local implementation is not enough to minimize the probability of harm, the AI developer would then evaluate whether changes to the underlying platform are needed. The developer would also need to consider how any fixes might affect other customers. They would both be contractually obligated to act within a contractually agreed upon timeframe to reduce the probability of that harm happening again.
The best people to design these outlier tests are subject-matter experts inside the user company. They understand what is normal for their business and what outlier situations could realistically occur in the future. Outside consultants could help guide the process, but they should not define the scenarios directly. The AI developer also should be excluded from designing the tests, since it could be argued that it would not be in their interest to uncover weaknesses in their own product.
Why would either side agree to this additional work?
The answer is insurance and liability. If harm occurs and a lawsuit follows, then both companies are potential plaintiffs. Through this ‘outlier testing’ process both companies could show that they took reasonable, documented steps to minimize the probability of problems. Courts tend to view these “good faith efforts” favorably, especially if an independent auditing firm corroborates the effort. Insurance companies, seeing that the risk of harm is lower, should respond by offering reduced AI liability premiums to both the developer and the user companies. Once one insurance company does this to differentiate their product and strive for competitive advantage, others will follow.
In this way, the insurance industry could become an unexpected driver of safer and more trustworthy AI. Over time, consumers would benefit from AI systems that are more reliable and less likely to cause harm.
This approach could be used anywhere in the world. Because it relies on simply adding a new clause for negotiation in the already-existent contract negotiation process between two parties, it does not depend on local regulations or international standards. It could be implemented immediately in the United States, Europe, China, or elsewhere, and could later be incorporated into broader local, regional, and global standards if desired.
AI developers may argue that this system would slow or “stifle” innovation. In reality, it would encourage a different kind of innovation. It would encourage building AI systems that meet clear performance and responsibility obligations. Once one developer adopts this approach, it becomes a competitive advantage. Similarly, once an insurer offers lower premiums for companies that follow these practices, adoption would accelerate across the market. So the response to AI developers is; by claiming innovation would be stifled through this approach, you are working to stifle innovation of more responsible and dependable AI.
This model also addresses ethical concerns without debating ethics directly. Laws reflect a society’s shared values, and insurance pricing reflects the cost of breaking those laws. By designing contracts that reduce legal risk, ethical behavior is built into the system automatically.
By moving away from one-sided click licenses and toward shared responsibility between AI developers and users, we can spread and reduce the risks as AI plays a larger role in our lives. This shared-liability approach offers a practical path toward safer, more dependable, and more reliable AI for everyone. It also rebalances the power to shape our AI future; from the few AI platform developers who are motivated to shape the platforms to serve their own interests, towards a contract with shared responsibility empowering their customer base and the global community of AI users to shape our AI-enabled future world.