AI systems are moving fast—from research labs to real-world deployment in finance, healthcare, law, and other high-stakes industries. Yet, while AI increasingly influences critical decisions, it still operates without a clear liability framework. When AI goes wrong, who’s responsible?
To build trust in autonomous systems, the AI industry must borrow a page from traditional risk management: digital Errors & Omissions (E&O) insurance. Just as E&O insurance protects professionals from claims of negligence or malpractice, a tailored version can help manage the unique risks of machine learning systems—before things go off the rails.
The Liability Gap in AI
AI’s accountability problem is well-known, and it stems from what scholars call the “problem of many hands.” Responsibility is often distributed across developers, data providers, hardware vendors, operators—and even the users themselves. This makes it difficult to assign blame when harm occurs.
Traditional liability frameworks fall short in the face of black-box models and unpredictable outcomes. For example, in 2018, a self-driving Uber vehicle in Arizona struck and killed a pedestrian. The car’s system had detected her but misclassified her as a non-human object—so it didn’t apply the brakes.
Untangling liability in this case was nearly impossible. Was it Uber’s fault? The developers of the AI? The car manufacturer? Or the pedestrian, who may have been jaywalking? This ambiguity illustrates the urgent need for a system that provides structured accountability and recourse.
Black Boxes and Legal Gray Zones
Most AI systems are notoriously opaque—even to their creators. Proprietary protections around training data and model architectures make it hard to understand how inputs become outputs. That’s a major issue when lives or livelihoods are on the line.
According to UCLA law professor Andrew D. Selbst, this lack of transparency makes outcomes hard to predict and even harder to litigate. AI often functions as an assistant—like in healthcare, where models analyze patient data to guide diagnoses. But when AI leads a doctor astray, who’s responsible for the mistake?
Selbst argues that AI complicates the traditional “duty of care” because it replaces human judgment with inscrutable code. This can limit professionals’ ability to anticipate harm—and leaves both humans and machines legally exposed.
Why AI Needs E&O Coverage
Traditional E&O insurance protects professionals and businesses from claims related to negligence, misrepresentation, or service failure. In AI, that could include:
- Chatbots delivering false or misleading information
- Predictive algorithms causing financial loss
- AI systems producing biased or discriminatory outcomes
- System failures that result in reputational or physical harm
As AI adoption scales, insurers are beginning to offer AI-specific E&O policies. These can help companies mitigate risk from hallucinations, data drift, or breakdowns in performance. For example, when Air Canada’s chatbot wrongly promised a passenger a discount, a tribunal ruled in the customer’s favor. If E&O insurance had been in place, it might’ve covered the cost—assuming the model’s error was within scope.
Challenges in Applying Traditional E&O to AI
However, AI’s complexity means the industry can’t just copy-paste existing insurance templates. Standard policies are often “silent” on AI, offering little coverage—or worse, leaving room for claim denials due to vague definitions.
An AI-blind Tech E&O policy might have a $10 million cap—but only allocate $50,000 to AI-related claims. Worse, attempts to define “AI failure” too tightly could introduce loopholes that insurers use to avoid payouts.
Instead, insurers and developers must work together to define realistic, measurable performance expectations. For example, if a chatbot is expected to maintain 90% accuracy but suddenly drops to 60%, coverage may be triggered. But if a model is unexplainable, unstable, or lacks proper guardrails, it may not be eligible for coverage at all.
From Risk Mitigation to Incentive Alignment
This selective underwriting could have a powerful ripple effect. To qualify for E&O coverage, companies will need to show their models meet certain performance, explainability, and safety standards. That means:
- Better documentation
- More robust monitoring tools
- Auditable training data
- Clearer deployment protocols
In short, insurance could become a market-driven incentive for responsible AI development.
At the same time, companies hesitant to adopt AI due to hidden risks and unclear liabilities would gain confidence from knowing those risks are shared, priced, and professionally managed. E&O insurance can offer peace of mind—not just for businesses, but for end users, regulators, and investors alike.
The Bottom Line
AI’s growing influence demands a modern accountability layer. E&O insurance—appropriately adapted for the AI age—could be a cornerstone of that system. It offers not just a financial safety net, but a framework for trust, performance standards, and continuous oversight.
As AI becomes embedded in more aspects of society, we’ll need more than great algorithms. We’ll need safety nets, standards—and yes, insurance. The time to build them is now.
