Skip to main content
Digital Compliance Bureau
Dark green background, "Weak Application Security Can Cost You Millions," 3 slanted images of fingers pointing to digital locks, and a "Learn the Basics" button
Category: AI Governance

Model Explainability

Also known as: Explainability, Interpretability
Simply put

Model explainability is the ability to describe how an artificial intelligence (AI) or machine learning (ML) system produces its outputs, in a way that makes sense to people. It helps users understand how and why a model is making its decisions, which can build trust, help uncover biases, and support efforts to improve model performance. Explainability may be provided at a broad level (across the model as a whole) or for an individual prediction.

Formal definition

Model explainability refers to the capability to characterize and communicate how an AI or ML model produces its predictions, encompassing both global explainability (understanding the model's overall behavior) and local explainability (understanding a specific individual output). It is closely related to, and sometimes used interchangeably with, interpretability, which concerns whether a model and its outputs can be explained in a way that makes sense to a human. Practitioner tooling (for example, feature-attribution methods that indicate how a model makes predictions) supports these objectives by helping users interpret and audit model decisions, surface potential biases, and inform model refinement. Note that the terms 'explainability' and 'interpretability' are not defined uniformly across sources, and specific techniques and their applicability depend on the model type and use case; readers should verify definitions against the latest authoritative source.

Why it matters

As organizations deploy AI and ML systems in consequential settings, the ability to describe how a model produces its outputs becomes central to accountability, trust, and oversight. When stakeholders cannot understand how or why a system reaches its conclusions, it is difficult to detect errors, challenge outcomes, or determine whether a decision rests on legitimate factors. Explainability supports these needs by making model behavior legible to people, whether at the level of the model as a whole or for an individual prediction.

Explainability also serves as a practical mechanism for surfacing bias. Because it helps users understand which factors drive a model's outputs, it can reveal when a system relies on inputs that produce unfair or unintended results, allowing teams to investigate and address them before or after deployment. In addition to fairness objectives, this same visibility can inform ongoing model refinement, helping practitioners diagnose weaknesses and improve performance over time.

It is worth noting that explainability is a technical and organizational capability, not in itself a legal standard. The extent to which explanation of automated decisions is legally required varies by jurisdiction, sector, and use case, and the terms used to describe these capabilities are not defined uniformly across sources. Readers should treat explainability as one component of responsible AI practice and verify any specific obligations against the applicable authoritative source.

Who it's relevant to

AI/ML Governance and Risk Teams
Teams responsible for AI oversight rely on explainability to characterize how deployed models behave, to audit individual decisions, and to surface potential biases. Both global and local explanations can support their efforts to understand and document model behavior, though the appropriate techniques depend on the specific model and use case.
Data Scientists and ML Practitioners
Those who build and maintain models use explainability tooling, such as feature-attribution methods, to interpret how and why a model makes decisions. This visibility helps them uncover biases and diagnose weaknesses, informing ongoing model refinement and performance improvement.
Compliance Officers and Auditors
Professionals evaluating AI systems may use explanations of model outputs to assess whether decisions rest on appropriate factors and to support review of automated processes. Because legal requirements around explanation of automated decisions differ across jurisdictions and use cases, they should confirm any specific obligations against the applicable authoritative source rather than assuming a uniform standard.
Stakeholders Affected by Automated Decisions
Users and other parties on the receiving end of model outputs benefit from explanations that make sense to people, which can help build trust and provide a basis for understanding or questioning a given decision. The level of explanation available depends on the model and the tooling applied to it.

Inside Model Explainability

Global explainability
Techniques and documentation that describe how a model behaves overall across its inputs, including which features generally drive its outputs and the model's decision logic at an aggregate level.
Local explainability
Methods that explain an individual prediction or decision for a specific input, addressing why a particular outcome was reached for a given subject rather than the model as a whole.
Interpretability versus post-hoc explanation
A distinction between models that are inherently interpretable by design (for example, simpler linear or rule-based models) and post-hoc explanation methods applied to complex or opaque models after the fact to approximate reasons for their outputs.
Explanation for affected individuals
Communication of meaningful information about the logic involved in an automated decision to the person subject to it, which in certain jurisdictions may connect to data protection rights around automated decision-making. The precise scope and form of such rights differ by jurisdiction and remain subject to evolving interpretation.
Technical documentation and traceability
Records describing the model's design, data sources, features, and evaluation, which support the ability to reconstruct and account for how outputs are produced. These may form part of obligations under certain regimes or voluntary frameworks, depending on context and risk level.
Audience-dependent explanation
Recognition that the appropriate depth and format of an explanation varies by recipient, for example a technical auditor, a regulator, a business owner, or an affected individual, each of whom may require different information.

Common questions

Answers to the questions practitioners most commonly ask about Model Explainability.

Is model explainability a legal requirement under regulations like the GDPR or the EU AI Act?
Explainability as a defined technical practice is not itself a standalone legal mandate under a single universal term. Rather, certain regulations create obligations that explainability techniques may help satisfy. For example, the GDPR contains provisions relating to automated decision-making and information about the logic involved, and the EU AI Act imposes transparency and documentation obligations that vary by risk classification. The precise scope, applicability thresholds, and whether these amount to a right to a full technical explanation remain subject to interpretation and evolving guidance. Voluntary frameworks and standards may also reference explainability, but those are not binding unless incorporated by law or contract. Readers should verify obligations against the current official text of the relevant regulation for their jurisdiction and sector, and treat this as informational rather than as legal advice for a specific situation.
Does an explainable model mean the model is also fair, accurate, or compliant?
No. Explainability describes the degree to which the reasoning behind a model's outputs can be understood or communicated; it is a distinct property from fairness, accuracy, robustness, and overall compliance. A model can be highly explainable yet produce biased or inaccurate outcomes, and a well-performing model may be difficult to explain. Explainability may support fairness assessments and compliance efforts by making certain behaviors visible, but it does not by itself establish any of those other properties. Conflating them can create a false sense of assurance. Each dimension generally requires its own evaluation, and application to particular circumstances requires professional judgment.
How should an organization decide what level of explainability a given model needs?
In most cases, the appropriate level depends on factors such as the risk associated with the use case, the categories of data involved, the impact on affected individuals, and any applicable regulatory or contractual obligations. Higher-stakes applications generally warrant more rigorous explainability measures, while lower-risk uses may need less. Some frameworks and regulatory schemes tie transparency expectations to a risk-based classification. Because requirements are fact-specific and interpretations continue to evolve, organizations should map their intended use to the relevant obligations and verify against current authoritative sources rather than applying a single fixed threshold across all models.
What is the difference between explaining an individual decision and explaining the model as a whole?
These are distinct objectives that often call for different techniques. Local explanations aim to clarify why the model produced a particular output for a specific input, which may be relevant where an affected individual seeks information about a decision. Global explanations aim to characterize the model's overall behavior and general logic, which may support governance, documentation, and audit-related activities. An approach adequate for one purpose may not satisfy the other, so organizations generally need to identify which obligation or objective they are addressing before selecting methods. What counts as sufficient in either case can depend on the applicable requirements and remains an area of evolving practice.
How can explainability be documented for audit or assessment purposes?
Documentation practices typically involve recording the methods used to generate explanations, the intended audience, the limitations of those methods, and how the explanations relate to the model's use case and associated risks. Note that an audit and an assessment are not the same: an audit generally refers to a more formal examination against defined criteria, while an assessment may be broader or internal. Explainability records may feed into either but do not by themselves constitute certification of any kind. Because expectations differ across frameworks, standards versions, and jurisdictions, organizations should confirm what documentation their applicable scheme or regulator expects and verify against the latest authoritative source.
What are the practical limitations of explainability techniques teams should account for?
Explainability methods often produce approximations of a model's behavior rather than exact accounts, and different techniques can yield different or even conflicting explanations for the same output. Some methods are sensitive to how inputs are configured, and an explanation that is technically valid may still be difficult for a non-specialist audience to interpret. There can also be tension between providing detailed explanations and protecting proprietary or security-sensitive information. Because of these constraints, explanations should be treated as one input among several rather than as definitive proof of how or why a model reached a result. The field continues to develop, and application to specific circumstances requires professional judgment.

Common misconceptions

Model explainability is a single, legally mandated standard that all organizations must meet in the same way.
There is no universal explainability requirement. Some obligations may arise from binding law in specific jurisdictions and sectors, while other expectations come from voluntary frameworks or contractual terms. The applicable requirements depend on jurisdiction, risk level, and use case, and readers should verify against the current official text relevant to their situation.
An explanation of a model's output guarantees that the model is fair, accurate, or compliant.
Explainability describes how or why a model produces outputs; it is distinct from fairness, accuracy, and legal compliance. A model can be explainable yet still biased or non-compliant, and explanations do not by themselves demonstrate that other obligations are met.
Post-hoc explanation methods reveal the model's true internal reasoning.
Post-hoc methods generally produce approximations or attributions of a model's behavior rather than a definitive account of its internal mechanics. Their outputs should be treated as interpretive aids whose reliability may vary, not as exact descriptions of causation.

Best practices

Identify the applicable obligations for your specific jurisdiction, sector, and use case, distinguishing binding legal requirements from voluntary frameworks or contractual commitments, and verify against the latest authoritative source.
Match the type of explanation to the audience, providing global-level documentation for auditors and regulators and clearer, plain-language local explanations for individuals affected by automated decisions where relevant.
Where feasible for higher-risk uses, consider inherently interpretable models before relying solely on post-hoc explanation methods applied to opaque systems.
Maintain technical documentation and traceability records covering data sources, features, and evaluation so that outputs can be accounted for and reconstructed as needed.
Treat post-hoc explanation outputs as approximations and document their known limitations rather than presenting them as definitive accounts of model reasoning.
Keep explainability efforts distinct from, but coordinated with, separate assessments of fairness, accuracy, privacy, and security, and involve qualified professionals to judge application to specific circumstances.
Promotional banner highlighting failures found in PCI audits and how to spot the gaps