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When an AIOps system flags an incident, operators need to ask: “Why did it flag this, and what evidence points to the root cause?” A useful diagnosis should expose its supporting signals, make sense to the person who must act, and acknowledge uncertainty—not merely present a confident-sounding label.
What the AIOps “black box” problem means
AIOps tools apply analytics or AI to operational data to identify anomalies, connect events, or suggest causes. The black box problem arises when people cannot tell what evidence led to a recommendation, whether that evidence supports the claimed cause, or when the system’s conclusion should not be trusted.
Three related terms help clarify what to ask for. NIST distinguishes transparency—what happened in a system—from explainability—how a decision was made—and interpretability—what an output means in its intended context. An event log can make system activity visible without explaining why a diagnosis was reached; an explanation can describe the decision process without telling a particular operator what the result means for their work. NIST recommends tailoring explanations to users’ roles, knowledge, and skills. See the NIST AI Risk Management Framework Knowledge Base.
1. Instrument services so there is evidence to inspect
An AIOps explanation can only be as useful as the operational evidence available to support it. Before an incident, ensure the relevant services emit and correlate logs, metrics, and traces. OpenTelemetry is a vendor-neutral framework for instrumenting, generating, collecting, and exporting telemetry.
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- Traces show a request’s path across services. Spans record individual units of work and can carry useful metadata.
- Logs capture events and contextual details; correlation with traces can help connect an event to a request or service operation.
- Metrics show numerical behavior over time, such as rates or resource measurements, and help establish when a system’s behavior changed.
These signals answer different questions. A trace can reveal where a request slowed down; metrics can show whether the slowdown coincided with a broader change; logs can supply event-level context. If the signals are missing or cannot be connected, an explanation may have little evidence to expose.
2. Make each diagnosis inspectable
Treat an AIOps root-cause recommendation as a hypothesis supported by evidence, not as an authoritative label. An operator should be able to move from the recommendation to the underlying records and judge whether the proposed cause fits the incident.
A practical diagnosis view should expose:
- The affected service, resource, or dependency and the incident time window.
- The signals that support the diagnosis, with timestamps and direct paths into relevant telemetry.
- Related dependencies and recent changes that may help explain the behavior.
- The reason the system connected those observations to the suggested cause.
NIST’s AI RMF Measure guidance calls for models to be explained, validated, documented, and interpreted in context. In product documentation, OpenText describes cross-signal investigation capabilities in AI Operations Management, and Microsoft describes investigation and traceable reasoning in Azure Monitor. These are vendor descriptions of their own services, not independent evidence that one platform performs better than another.
3. Match the explanation to the person using it
One explanation format will not serve every operational role equally well. An on-call engineer investigating an outage may need to inspect spans, timestamps, dependencies, and deployment context. A manager coordinating response may need to know which services and users are affected, how confident the recommendation is, and what action is proposed.
Design the explanation around the decision the recipient must make. Provide enough detail to verify the conclusion, but do not assume that every reader needs the same technical view. NIST’s guidance treats meaningful transparency as information suited to the lifecycle stage and the role or knowledge of the person receiving it. It also distinguishes an account of a system’s mechanisms from the meaning of its output in context.
“But an explanation that would satisfy an engineer might not work for someone with a different background.” — P. Jonathon Phillips, NIST electronic engineer and co-author of NISTIR 8312, in NIST’s August 18, 2020 report announcement.
4. Test whether explanations are faithful and useful
Fluent language is not proof of a good explanation. Test whether the stated reason reflects the process that actually generated the output, whether the cited evidence supports the claimed cause, and whether intended users can understand the explanation well enough to act.
NISTIR 8312, published September 29, 2021, describes four principles for explainable AI: explanation, meaningfulness, explanation accuracy, and knowledge limits. NIST’s AI RMF Measure guidance recommends testing explanations with relevant AI actors and end users. It also advises documenting details such as model type, features, thresholds, training and evaluation data, and ethical considerations. See the NIST AI RMF Measure function and NISTIR 8312.
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For an operational team, that means testing an explanation against concrete questions: Can responders locate the cited telemetry? Does changing or removing a supposedly important input alter the recommendation in an expected way? Can the intended users distinguish evidence from inference and identify an appropriate next step? Record the results rather than judging quality from a polished demo or a single successful incident.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Show uncertainty and keep limits visible
A recommendation should not imply certainty when the system is operating beyond conditions it was designed for, lacks sufficient evidence, or cannot confidently distinguish among possible causes. NIST’s knowledge-limits principle says systems should operate within their design conditions and when they have sufficient confidence. Make it possible for operators to recognize uncertainty, investigate further, and take over safely.
Keep records current as models, data, thresholds, and operating conditions change. Document known limits and evaluation results so teams can debug, monitor, audit, and govern the system over time. Explainability is not a one-time interface feature: the evidence, model behavior, and operational context behind a recommendation can change.
How to evaluate an AIOps explanation approach
Use these criteria when assessing a platform or designing an internal workflow. They combine NIST’s explainability guidance with OpenTelemetry’s model of complementary telemetry; they are evaluation criteria, not a ranking of vendors.
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| Evaluation area | What to check |
|---|---|
| Evidence provenance | Can an operator trace the recommendation to specific logs, metrics, traces, dependencies, and changes? |
| Fidelity | Does the explanation accurately reflect the process that produced the system’s output? |
| Audience fit | Can the intended operator understand the explanation and use it to make a decision? |
| Uncertainty and limits | Does the system expose low confidence, missing evidence, or conditions outside its intended use? |
| Signal coverage | Can the workflow correlate relevant telemetry and operational context rather than relying on one signal alone? |
| Validation and governance | Are explanations tested with users, and are model, data, evaluation, and known-limit records maintained? |
NIST’s AI RMF materials cited here are based on AI RMF 1.0, and NIST indicates that a revision is in progress. Its guidance and vendor product documentation may change; check the current source material when applying it to a deployment.
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