Open Data Infrastructure
AI-Ready Data Negative Evidence Sets
How negative evidence tells agents when approved data does not support an answer, action, or regulated decision.
AI systems need evidence for refusal, not only evidence for answers.
Unsupported answers need a data product too
Most AI-ready data work focuses on the positive path. Which source supports the answer? Which metric should the agent use? Which context should retrieval return? That is necessary. It is only half the control problem.
Agents also need to know when approved data does not support an answer, action, metric, recommendation, or regulated decision. A negative evidence set is the structured evidence that tells the system what is missing, stale, out of scope, denied, or contradicted.
Core idea: AI-ready data needs negative evidence sets so agents can refuse with evidence instead of inventing confidence.
Risk management depends on traceable evidence
The NIST AI Risk Management Framework gives organizations a structure for managing AI risk. W3C PROV describes provenance as information about entities, activities, and people involved in producing data or things, useful for assessing quality, reliability, and trustworthiness.
Negative evidence applies that same discipline to refusal. The system should know which approved sources were checked, which policy denied access, which freshness window failed, which metric was undefined, or which authority was missing.
Design refusal as a first-class data state
- Define standard negative evidence types such as missing authority, stale source, policy denial, insufficient sample, and conflicting source.
- Attach source checks, policy decisions, timestamps, and owner routes to each refusal.
- Expose refusal evidence to users without leaking restricted data.
- Test negative examples during evaluation, not only correct answers.
- Measure when agents answer despite negative evidence.
This extends the four layers of AI-ready data infrastructure, retrieval governance, and AI audit packet design. The refusal path is part of the product.
What breaks first
- The agent answers with a nearby metric because the correct metric was unavailable.
- Policy denial is treated as missing data instead of a governed refusal.
- Freshness failure is hidden behind a confident response.
- Evaluation tests only the happy path and never checks refusal behavior.
Questions to ask before trusting the agent
- Which negative evidence types can the system represent?
- Can the agent explain why approved data does not support the requested action?
- Which owner reviews repeated refusal patterns?
- Do evaluations include missing, stale, denied, and contradictory cases?
Sources to start with
These primary sources anchor the technical claims in this guide.
A system that cannot say no with evidence is not ready to say yes.