Uncertainty Isn't One Thing: How to Triage Ambiguity vs. Knowledge Gaps

New research from the University of Arizona has quantified something we've been observing in financial services AI deployments: when your AI system says it's uncertain, that uncertainty comes from fundamentally different sources that require fundamentally different responses.

The researchers, led by Yang Nan and colleagues, made a simple but powerful discovery. By generating multiple responses from the same language model and analyzing how those responses disagree with each other, you can diagnose whether the model is uncertain because the question is ambiguous or because it lacks the necessary knowledge to answer.

This distinction matters enormously in financial services, where the cost of uncertainty is measured in escalations, compliance risks, and customer frustration.

What the Research Shows

The research team's approach was elegant. They took questions that made language models uncertain and generated multiple responses, typically around ten. Then they had another model analyze the patterns of disagreement among those responses.

When models were uncertain due to question ambiguity, their multiple responses would each answer a different interpretation of the question. Ask about "the prime minister of Canada in 1920" and responses might confidently name different people, each correct for different parts of that year. The model has the knowledge but doesn't know which interpretation you want.

When uncertainty came from knowledge gaps, the responses showed a different pattern. They'd hedge, generalize, or provide conflicting information while dancing around the missing knowledge. Ask about specific chemical reactions or physics calculations the model hasn't seen, and you get educated guesses that reveal the absence of real understanding.

The researchers validated this on multiple datasets, finding that clarifying ambiguous questions reduced uncertainty by 27-67%, while providing missing knowledge improved accuracy by up to 42%. These aren't marginal improvements.

Why This Matters for Financial Services

In financial services AI, we see these patterns every day. A customer asks your lending chatbot "what's the rate?" The AI might be uncertain because the question is ambiguous (mortgage rate? personal loan? refinancing? purchase?), or because it genuinely doesn't have access to current rates. Traditional uncertainty metrics treat these identically. Both trigger escalations. Both frustrate customers. But they need completely different fixes.

Consider transaction monitoring systems. When your compliance AI flags a transaction as uncertain, is it because the transaction description is ambiguous ("AMZN MARKETPLACE" could be personal or business), or because it lacks information about that specific merchant code? The answer determines whether you need better classification rules or more comprehensive merchant databases.

The research shows that many cases involve both types of uncertainty. A question about international wire fees might be ambiguous (which country? what amount? which account type?) while also revealing gaps in your fee schedule documentation. The researchers found these cases need sequential handling: first reduce ambiguity to narrow the problem space, then address the specific knowledge gap.

How Uncertainty Patterns Reveal System Issues

The distribution of uncertainty types in your AI system tells you exactly what's wrong. High rates of ambiguity-driven uncertainty point to poor interface design or missing context gathering. Your AI has the knowledge but can't figure out what users are asking for.

High rates of knowledge gap uncertainty reveal documentation problems or disconnected systems. Your AI understands the questions perfectly but lacks the information to answer them. This is particularly common in financial services where critical information lives in separate systems that don't talk to each other.

The researchers found that tracking these patterns over time shows whether your improvements are working. If you're adding documentation but ambiguity rates stay high, you're solving the wrong problem. If you're improving interfaces but knowledge gap rates persist, you need to focus on information retrieval, not user experience.

The Path from Research to Production

The research provides a clear methodology that maps directly to production systems. For high-stakes decisions in lending, investment advice, or compliance, generating multiple internal responses adds minimal latency but provides crucial diagnostic information.

When those responses show different interpretations, implement clarification. "I can help with rates. Are you asking about mortgages, personal loans, or auto loans?" When they show knowledge gaps, either retrieve from authoritative sources or escalate with specific context about what's missing.

The key insight is that your QA dashboards should track these as separate metrics. Clarification rates by topic show where your interfaces need work. Knowledge gap frequencies by domain show where documentation or integration efforts should focus. Resolution success rates validate that you're diagnosing correctly.

What This Means for Knowledge Management

Understanding uncertainty sources transforms how you think about knowledge bases. The research shows that throwing more documentation at ambiguity problems won't help. If users are asking ambiguous questions, you need structured forms, better context gathering, or clearer interfaces.

For genuine knowledge gaps, the research validates a targeted approach. Instead of trying to document everything, you can identify exactly which missing knowledge causes the most uncertainty. The patterns in your AI's uncertain responses become a roadmap for documentation priorities.

This is particularly powerful in financial services where documentation is extensive but often contradictory. When your AI shows uncertainty, you can now diagnose whether different departments have conflicting documentation (the responses will contradict each other) or whether information is simply missing (the responses will be vague and hedging).

The Organizational Impact

The research has implications beyond technical implementation. It shows that different types of uncertainty should route to different teams. Ambiguity issues need product and UX teams to improve interfaces. Knowledge gaps need subject matter experts and technical teams to improve documentation and integrations.

This changes how you structure your AI operations. Instead of a general "AI accuracy" metric that everyone is vaguely responsible for, you get specific, actionable metrics that clearly belong to specific teams. Product owns ambiguity rates. Knowledge management owns gap frequencies. Everyone knows exactly what they need to fix.

It also changes how you prioritize improvements. The research shows that fixing ambiguity is usually faster and cheaper than filling knowledge gaps. A simple form that collects necessary context can eliminate thousands of escalations. But for knowledge gaps, you need systematic documentation efforts or system integrations.

The Continuous Improvement Cycle

The research demonstrates that uncertainty diagnosis should be continuous, not a one-time analysis. As you fix ambiguity issues, knowledge gaps become more visible. As you fill knowledge gaps, new ambiguity patterns emerge.

In financial services, this matches the reality of constant change. New products launch, regulations update, customer behaviors evolve. Your uncertainty patterns become an early warning system for these changes. A spike in ambiguity might mean customers are asking about your products in new ways. A surge in knowledge gaps might mean your documentation hasn't kept pace with product changes.

The researchers' approach of using an auxiliary model to diagnose uncertainty is particularly elegant. It means you don't need to modify your production systems. You can sample uncertain responses, run the diagnosis offline, and use the results to guide improvements.

Beyond Simple Escalation

The traditional approach to AI uncertainty in financial services is simple: when confidence is low, escalate to a human. The research shows this is wasteful and often wrong. Many uncertain cases just need clarification, not escalation. Many knowledge gaps can be filled by connecting to existing systems, not human intervention.

By implementing uncertainty triage, you transform your AI from a binary system (confident or escalating) into an intelligent assistant that knows when to clarify, when to retrieve additional information, and when to genuinely need human help. This doesn't just reduce escalations. It improves customer experience by providing faster, more appropriate responses.

The research also reveals why some AI improvements fail. If you're training your model on more data but the uncertainty comes from ambiguous questions, you're wasting resources. If you're improving prompts but the uncertainty comes from missing knowledge, you won't see improvements. Diagnosis must come before treatment.

The Competitive Advantage

Financial institutions that understand uncertainty sources have a significant advantage. They can fix problems faster because they know exactly what's wrong. They can improve more efficiently because they're not wasting effort on the wrong solutions. They can scale more confidently because they understand their AI's limitations precisely.

The research from the University of Arizona gives us a scientific foundation for what we've observed empirically: uncertainty isn't monolithic. By diagnosing its sources and implementing targeted responses, you transform vague confidence scores into precise operational insights.

In financial services, where accuracy and compliance are non-negotiable, this isn't just an optimization. It's the foundation of trustworthy AI. The question isn't whether your AI will be uncertain (it will), but whether it handles that uncertainty intelligently. Understanding the difference between "I don't understand the question" and "I don't know the answer" might be the most important distinction your AI system can make.