Category: Explainability & Interpretability

Explainable AI

Also known as: XAI, Explainable artificial intelligence

Simply put

Explainable AI (XAI) refers to a set of methods and techniques intended to help people understand how and why an AI system produced a particular output. The goal is to make model behavior more transparent so that humans can exercise meaningful oversight, rather than treating the system as an opaque 'black box.' In practice, XAI is used to help characterize aspects such as a model's accuracy, fairness, and potential biases, though the depth and reliability of any given explanation can vary.

Formal definition

Explainable AI (XAI) denotes a field of research and an associated collection of procedures and techniques that enable machine learning algorithms to produce outputs accompanied by human-intelligible rationale, supporting intellectual oversight of AI systems and answering 'how' and 'why' questions about their behavior. XAI methods are commonly applied to characterize model accuracy, fairness, transparency, and potential biases. Note that XAI as commonly used encompasses a range of approaches whose scope and specific meaning are not standardized across all frameworks; readers should also distinguish explainability (typically framed as producing human-understandable justifications for a model's outputs, often post hoc) from interpretability (the degree to which a model's internal mechanics are inherently understandable), as practitioners frequently treat these as related but distinct concepts. This entry does not resolve those definitional distinctions, which vary by context and source.

Why it matters

Explainable AI matters because oversight of an AI system depends on someone being able to understand how and why it produced a given output. When a model behaves as an opaque 'black box,' it becomes difficult for reviewers to assess whether an output is sound, whether the model is relying on inappropriate factors, or whether its behavior is consistent with an organization's obligations. XAI methods are commonly used to help characterize a model's accuracy, fairness, transparency, and potential biases, giving humans a basis for meaningful intellectual oversight rather than unexamined reliance.

For professionals in AI governance and model risk management, explainability supports several distinct functions that should not be collapsed into one another. In governance terms, explanations can help satisfy accountability and oversight expectations by making model behavior reviewable. In model risk terms, they can inform validation and ongoing monitoring by helping reviewers probe why a model produces particular results. It is important to note that these are related but separate uses: an explanation that aids a governance sign-off does not by itself establish that a model's risk has been measured or controlled.

At the same time, XAI should not be treated as a guarantee. The depth and reliability of any given explanation can vary, and the term encompasses a range of approaches whose scope and meaning are not standardized across frameworks. Explanations reduce opacity and support oversight; they do not eliminate model risk, and a plausible-sounding rationale is not proof that a model is accurate or fair. Practitioners should also keep in mind the distinction between explainability, typically framed as producing human-understandable justifications for outputs (often after the fact), and interpretability, the degree to which a model's internal mechanics are inherently understandable. Conflating the two can lead to overconfidence in what a particular explanation actually demonstrates.

Who it's relevant to

Model risk managers and validators

Explanations can support the review of a model by helping validators probe why it produces particular outputs and by informing assessments of accuracy and potential biases. Practitioners in this role should treat explainability as an input to validation and monitoring rather than as evidence that risk has been measured or controlled, and should keep validation (independent challenge of whether a model is fit for purpose) distinct from the explanation techniques themselves.

AI governance and compliance officers

For those responsible for organizational oversight, XAI methods can help make model behavior reviewable and support accountability structures. Governance professionals should note that an explanation supporting a sign-off addresses transparency and oversight, and does not by itself establish that model risk has been managed.

Data scientists and model developers

Developers apply XAI procedures and techniques to characterize a model's accuracy, fairness, transparency, and potential biases during and after development. Because these methods vary in scope and reliability, developers should be able to describe what a given explanation does and does not demonstrate, and should distinguish explainability (justifying outputs) from interpretability (inherently understandable model mechanics).

Auditors and reviewers

Auditors use explanations as a means of exercising intellectual oversight over AI systems by asking how and why an output was produced. They should evaluate the reliability of the explanation rather than accept a plausible rationale as proof of sound model behavior, since the depth and dependability of XAI outputs can vary.

Legal and policy specialists

Legal and policy professionals encounter explainability where transparency and oversight expectations arise. Because the term is not standardized across all frameworks and its regulatory treatment varies by jurisdiction and sector, these specialists should be cautious about assuming any single definition or requirement applies universally.

Inside XAI

Post-hoc explanation methods

Techniques applied after a model produces an output to approximate why it reached a given result, commonly including feature attribution methods such as SHAP or LIME. These generate approximations of model behavior rather than exact descriptions of internal mechanics.

Global versus local explanations

Global explanations describe overall model behavior across the population of inputs, while local explanations address why a specific individual prediction was made. Practitioners typically distinguish the two because a method suited to one is not necessarily reliable for the other.

Interpretability versus explainability

As commonly framed, interpretability refers to the degree to which a model's internal mechanics are inherently understandable (for example, a linear model or shallow decision tree), whereas explainability often refers to the use of external methods to render an otherwise opaque model's outputs understandable. These are related but distinct and are frequently blurred in practice.

Intended audience of the explanation

Explanations may target different stakeholders such as model developers, validators, business users, regulators, or affected individuals. The appropriate form and depth of explanation typically varies by audience, so a single explanation rarely serves all purposes.

Relationship to governance and oversight

Explainability can support AI governance objectives (such as accountability and oversight) and model risk management activities (such as validation review and effective challenge), but as a technique it is one input among several rather than a governance control in itself.

Common questions

Answers to the questions practitioners most commonly ask about XAI.

Is explainability the same thing as interpretability?

Not as most practitioners use the terms, though usage is not fully standardized. Interpretability typically refers to the degree to which a model's internal mechanics are inherently understandable to a human (for example, a linear model or a shallow decision tree whose logic can be inspected directly). Explainability, or XAI, more commonly refers to techniques applied to make a model's outputs or behavior understandable—often post hoc—regardless of whether the underlying model is itself transparent. A complex model can be subjected to explainability methods without being interpretable in this narrower sense. Because definitions vary across sources, it is worth stating which meaning you intend in any given document.

Does producing an explanation for a model mean the model is unbiased or fair?

No. Explainability addresses whether a model's behavior can be described or understood; it does not by itself establish that outcomes are unbiased or fair. Bias and fairness are distinct concepts, and fairness in particular is contested and can be defined in multiple, sometimes mathematically incompatible, ways depending on context. An explanation may even help surface a problematic feature reliance, but the explanation is a diagnostic aid, not evidence of fairness. Assessing bias and fairness typically requires separate analysis beyond generating explanations.

How does explainability relate to model validation activities?

Explainability techniques are often used as one input to validation, but they do not constitute validation on their own. In many model risk contexts, validation involves evaluating conceptual soundness, testing outcomes, and ongoing monitoring. Explanations can support the conceptual soundness review by helping assessors reason about whether a model relies on plausible drivers, and they can aid outcome analysis. However, explainability outputs should be treated as evidence to be evaluated, not as conclusions, and their own reliability and limitations should be assessed as part of the review.

Should the type of explanation differ depending on the audience?

Typically yes. Different stakeholders—model developers, validators, business users, auditors, affected individuals, and regulators—often need different forms and levels of explanation. A developer may need feature-level diagnostics, while a business user or affected individual may need a concise, plain-language account of the factors influencing a decision. Matching the explanation's technical depth and format to the audience's purpose is a common practical consideration, and organizations frequently document which explanation type serves which use.

What are common limitations to account for when relying on post hoc explanation methods?

Post hoc explanation methods can produce approximations of model behavior rather than exact accounts, and different methods can yield different or even conflicting explanations for the same output. Their outputs may be sensitive to configuration choices, sampling, and the data used to generate them. Because of these limitations, practitioners often document the method used, its assumptions, and its known constraints, and avoid treating a single explanation as a definitive statement of why a model produced an output.

How can explainability outputs be documented so they are useful for oversight and audit?

It is common to record the explanation technique applied, the model and version it was applied to, the inputs and time period covered, the intended audience and purpose, and the known limitations of the method. Documenting how explanations were used—for example, as input to validation, monitoring, or a decision-review process—helps oversight functions and auditors trace how the outputs informed decisions. Clear versioning and dating support reproducibility and later review, though specific documentation expectations vary by organization and applicable framework.

Common misconceptions

Explainability and interpretability mean the same thing.

As commonly defined, interpretability refers to a model being inherently understandable through its structure, while explainability often refers to applying external methods to make opaque model outputs understandable. Experts generally keep these distinct, and conflating them can obscure whether an explanation reflects actual model mechanics or only an approximation.

A post-hoc explanation reveals the true reason a model made a decision.

Post-hoc methods typically produce approximations of model behavior and can be sensitive to method choice and configuration. They should be treated as aids to understanding rather than exact accounts of internal model logic.

Producing explanations satisfies regulatory or governance requirements on its own.

Explainability may support governance and model risk management objectives, but it is one measure among several and does not by itself ensure accountability, oversight, or compliance. Its adequacy generally depends on the intended audience and use context.

Best practices

Select explanation methods according to the intended audience and purpose, recognizing that a form suited to developers may not serve business users, regulators, or affected individuals.

Distinguish clearly whether you are relying on inherent interpretability or on post-hoc explainability, and document which applies to each model.

Treat post-hoc explanations as approximations, and assess their sensitivity to method choice and configuration before relying on them.

Match the scope of the explanation to the question being asked, separating global explanations of overall behavior from local explanations of individual predictions.

Position explainability as one input to governance and model risk management activities such as validation review, rather than as a standalone control.

Document the limitations of the explanation methods used so that reviewers and stakeholders understand what the explanations can and cannot support.