Explainable AI Explainer
Explainable AI, often shortened to XAI, refers to methods and system designs that help people understand an AI system’s outputs or behavior. An explanation might identify influential features, summarize a decision path, show similar examples, state uncertainty, or describe how a model behaves across many cases.
Local explanations address one prediction, while global explanations describe broader patterns in the model. Some models are interpretable by design; others require post-hoc tools that approximate which inputs mattered. The right explanation depends on the audience, from a model developer to a person affected by a decision.
Explanations can support debugging, contestability, safety review, regulatory compliance, and informed human oversight. They are most useful when tied to an actual question, such as what evidence changed the result, whether the same rule is applied consistently, or what would have to change for a different outcome.
A clear explanation is not proof that the model is accurate or fair. Post-hoc methods can be unstable, oversimplified, or persuasive without faithfully representing the model. Explanations should be tested for correctness, consistency, usefulness, and audience comprehension alongside the performance of the underlying system.
Explainable AI uses methods and system design to help people understand why an AI produced a particular output or behavior.
Key terms include explainable AI, interpretability, transparency, local explanation, global explanation, feature attribution, counterfactual, fidelity and contestability.
<a href="https://www.nist.gov/publications/four-principles-explainable-artificial-intelligence" target="_blank" rel="noopener noreferrer">NIST: Four Principles of Explainable Artificial Intelligence</a> and <a href="https://nvlpubs.nist.gov/nistpubs/ir/2021/NIST.IR.8312.pdf" target="_blank" rel="noopener noreferrer">NIST IR 8312</a> provide the primary references for this explainer.
A local explanation focuses on one output; a global explanation summarizes patterns across many inputs. Different users may need different levels of detail.
Feature attribution, examples, decision rules, counterfactuals, and uncertainty displays answer different questions and should not be treated as interchangeable.
A post-hoc explanation may only approximate a complex model and can change with small input or method choices.
Evaluate fidelity, stability, user understanding, and whether the explanation helps people identify and challenge errors.
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