Explainability
Expanded definition
Explainability covers the evidence and methods used to help people understand an AI system's behavior. Depending on the system, that evidence can include feature attributions, example-based explanations, retrieval citations, tool traces, counterfactual tests, or documented decision rules. In generative AI products, practical explainability often means showing which sources grounded an answer, exposing tool calls, documenting prompt and policy constraints, and reporting evaluation results. These signals help with debugging, compliance review, and user trust, but they do not provide perfect causal access to a neural network's internal reasoning. Post-hoc explanations can be incomplete or misleading when treated as ground truth. Teams should pair explanations with representative evaluations, uncertainty communication, human review, and a clear statement of what each explanation method can and cannot establish.
Common explainability methods
Different methods explain different parts of an AI system. Select the method based on the question, audience, and risk being reviewed.
| Method | What it shows | Examples | Typical output |
|---|---|---|---|
| Feature attribution | Estimates how input features contributed to a prediction under a specific attribution method. | SHAP, Integrated Gradients, saliency maps | Feature scores or visual overlays |
| Example and counterfactual explanations | Shows similar examples or input changes that would alter the result. | Nearest examples, counterfactual inputs | Reference cases or changed-input scenarios |
| Evidence and execution traces | Exposes retrieved sources, tool calls, and workflow steps used to produce an answer. | RAG citations, agent traces, tool logs | Source links and step records |
| Behavioral evaluation | Tests outputs across controlled cases to document patterns, failures, and uncertainty. | Slice tests, red-team suites, regression evals | Metrics, failure examples, and review notes |
Explainability vs interpretability
| Dimension | Explainability | Interpretability |
|---|---|---|
| Primary question | What evidence helps a person understand this output or behavior? | How directly can a person understand the model or mechanism itself? |
| Common approach | May use post-hoc methods, citations, traces, or behavioral tests. | Often relies on transparent models, components, or mechanisms. |
| Practical GenAI use | Show sources, tool actions, constraints, evaluations, and uncertainty. | Study model structure or use simpler components whose behavior is easier to inspect. |
| Main limitation | A plausible explanation may be incomplete or method-dependent. | Full interpretability is difficult for large neural networks. |
Use explainability techniques to provide evidence appropriate to the decision, and use interpretability where transparent mechanisms are available. Neither replaces evaluation or human review.
Related terms
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Explainability FAQ
What is Explainability?
Explainability describes how clearly people can understand why an AI system produced a particular output or decision.
How is Explainability used in AI systems?
Explainability covers the evidence and methods used to help people understand an AI system's behavior. Depending on the system, that evidence can include feature attributions, example-based explanations, retrieval citations, tool traces, counterfactual tests, or documented decision rules. In generative AI products, practical explainability often means showing which sources grounded an answer, exp...
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