Category: Explainability & Interpretability

ISO/IEC TS 6254 (Explainability Objectives)

Also known as: ISO/IEC TS 6254, ISO/IEC TS 6254:2025, Objectives and approaches for explainability of ML models and AI systems

Simply put

ISO/IEC TS 6254 is a technical specification published by ISO and IEC that describes methods for making machine learning models and AI systems easier to explain to the various people who rely on them. It focuses on identifying what different stakeholders need to understand about an AI system and how those explainability goals can be met at different points in the system's life cycle. As a technical specification, it is a voluntary document intended to guide practice rather than a binding law.

Formal definition

ISO/IEC TS 6254:2025 is a technical specification, jointly issued by ISO and IEC, that describes approaches and methods for achieving stakeholders' explainability objectives with respect to machine learning (ML) models and AI systems. Per the evidence, it frames explainability as serving an overarching goal of evaluating (and, in earlier drafting language, improving) the trustworthiness of AI systems, and it recognizes that diverse stakeholders have differing explainability needs across stages of the AI system life cycle. As a Technical Specification (TS) rather than a full International Standard, it is a voluntary instrument and does not itself impose legal or regulatory obligations; practitioners should note it addresses explainability objectives and approaches specifically, and should not treat it as equivalent to jurisdictional regulation or as a model risk management control framework. The distinction between explainability and interpretability, and the precise scope of methods covered, are matters governed by the document's own text, which is not reproduced in full in the evidence provided here.

Why it matters

Explainability has become a central concern in AI governance and, in some sectors, in model risk management, because stakeholders who rely on an AI system's outputs often need to understand how and why those outputs are produced. ISO/IEC TS 6254 matters because it attempts to organize this concern into a structured set of objectives and approaches, recognizing that a data scientist, an affected end user, an auditor, and a regulator do not all need the same kind of explanation. By framing explainability around stakeholder needs across the AI system life cycle, the specification gives practitioners a common vocabulary for a domain where terminology and expectations have historically varied widely between organizations.

The document's own framing ties explainability to the overarching goal of evaluating the trustworthiness of AI systems (with earlier drafting language referring to improving trustworthiness). This positions the specification as a tool for supporting broader assurance activities rather than as an end in itself. For teams building governance programs, having a referenceable, internationally developed articulation of explainability objectives can help justify design and documentation choices to internal oversight functions and external parties.

It is important, however, to be precise about what this instrument is and is not. As a Technical Specification jointly issued by ISO and IEC, it is a voluntary document intended to guide practice; it does not itself impose legal or regulatory obligations, and it should not be treated as equivalent to jurisdictional regulation such as the EU AI Act, nor as a model risk management control framework in the sense of supervisory guidance like SR 11-7. Adopting or referencing TS 6254 does not, on its own, demonstrate compliance with any binding requirement, and it does not eliminate model risk; at most it supports explainability practices that may contribute to a wider governance or risk program.

Who it's relevant to

AI governance and policy specialists

Those responsible for organizational AI oversight can use TS 6254 as a reference point for defining explainability expectations across stakeholder groups and life cycle stages. It offers a structured vocabulary that can inform internal policy, though it should be positioned as voluntary guidance that supports a governance program rather than as a substitute for binding regulatory obligations.

Data scientists and ML engineers

Practitioners designing and building ML models can consult the specification for approaches and methods aimed at meeting stakeholders' explainability objectives at different points in the system life cycle. Because the full method catalog is set out in the document's own text and not reproduced in the evidence here, teams should work from the source specification directly.

Model validators and auditors

Second- and third-line functions evaluating AI systems may find TS 6254 useful for framing what explainability should achieve for different audiences and how it relates to evaluating trustworthiness. Validators should note, however, that the specification addresses explainability objectives and approaches specifically and is not itself a model risk management control framework.

Compliance and legal professionals

Those mapping standards to regulatory expectations should treat TS 6254 as a voluntary international technical specification that does not impose legal obligations on its own. It may inform explainability practices that support compliance efforts, but it should not be conflated with jurisdictional regulation, and referencing it does not by itself establish legal compliance.

Inside ISO/IEC TS 6254

Explainability objectives framing

The document is oriented around articulating objectives and approaches for explaining the behavior of machine learning models and AI systems, rather than mandating a single technical method. As commonly understood, it treats explainability as a goal to be defined relative to stakeholders and use context.

Stakeholder-relative explanation needs

Explainability objectives are typically framed with reference to who needs the explanation (for example developers, operators, affected individuals, auditors) and what decision or task the explanation supports, since an explanation adequate for one audience may not serve another.

Concepts and terminology

Technical specifications of this type generally seek to establish shared vocabulary so that practitioners distinguish related notions consistently. Note that explainability and interpretability are distinct concepts and should not be treated as interchangeable within such terminology.

Relationship to broader AI standards

As a technical specification under the ISO/IEC JTC 1/SC 42 body of work on artificial intelligence, it sits alongside other AI standards addressing governance and risk; the specific normative status and interplay depend on the published text and should be verified against the source.

Common questions

Answers to the questions practitioners most commonly ask about ISO/IEC TS 6254.

Is ISO/IEC TS 6254 a mandatory certification standard that organizations must comply with?

No. As a Technical Specification (the "TS" designation), it is generally positioned differently from a full International Standard and is not, in itself, a binding legal requirement. Adoption is typically voluntary unless a regulator, contract, or internal policy specifically references it. It also is not structured as a certifiable management system standard in the way ISO/IEC 42001 is commonly described, so treating it as a compliance checkbox would misapply its intended role.

Does following the explainability objectives in TS 6254 make an AI model interpretable or eliminate the need for interpretability techniques?

Not necessarily. Explainability and interpretability are distinct concepts that professionals are careful not to blur. Explainability, as commonly framed, concerns producing understandable accounts of a model's behavior or outputs, often through post-hoc methods, while interpretability more often refers to the degree to which the model's internal mechanics are inherently understandable. Addressing explainability objectives does not automatically render a model interpretable, and the two may require different approaches depending on the system and audience.

How do we identify which explainability objectives are relevant for a specific AI system?

In many approaches, objectives are selected based on the intended audience (for example, developers, affected individuals, auditors, or regulators) and the purpose the explanation serves, such as debugging, oversight, or contestability. The specific objectives, terminology, and prioritization should be taken from the text of the specification itself and mapped to your use case rather than assumed; where your context differs from the examples, document the rationale for the objectives you select.

Where does work against these explainability objectives fit within model risk management and governance activities?

Explainability work commonly supports both AI governance (oversight, accountability, and documentation structures) and model risk management (measurement, monitoring, and control of model-related risks), but it does not replace either. It can feed validation and review activities and support second-line challenge, while the accountability and policy structures that decide when and how explanations are used typically sit within governance. The distinction between these functions should be preserved rather than collapsed.

What documentation should we produce to demonstrate that explainability objectives were considered?

Typically, organizations record which objectives were identified as relevant, the reasoning for their selection, the intended audiences, the methods used to meet them, and any limitations of those methods. The exact documentation expectations should be drawn from the specification's own provisions and any applicable internal or regulatory requirements; this entry does not assert a fixed documentation format, and requirements may vary by sector and jurisdiction.

How should explainability objectives be maintained after a model is deployed?

Explainability needs can change as a model, its data, or its use context evolves, so many governance approaches treat explanation methods as subject to periodic review alongside monitoring for performance change. It is worth distinguishing this from performance degradation itself: explanations may need updating even where performance is stable, and vice versa. Any specific review cadence or trigger conditions should be defined by your own policies rather than inferred, as the specification's scope on ongoing operation should be confirmed against its actual text.

Common misconceptions

ISO/IEC TS 6254 is a binding regulatory requirement that organizations must comply with.

ISO/IEC technical specifications are voluntary consensus standards issued by ISO and IEC, not law. Adoption may be referenced by regulators or contracts, but the specification itself does not carry the force of a statute such as an AI-specific regulation. Treat its requirements as voluntary unless a specific legal or contractual obligation incorporates it.

Explainability and interpretability mean the same thing, so achieving one satisfies the other.

These are distinct concepts that experts keep separate. Explainability commonly refers to producing understandable accounts of a system's outputs or behavior for a given audience, while interpretability commonly refers to the degree to which the internal mechanics of a model can be understood directly. Meeting explainability objectives does not automatically make a model interpretable.

Meeting explainability objectives guarantees a model is fair, unbiased, or low risk.

Explainability supports understanding and oversight but does not eliminate risk, nor does it by itself establish fairness or the absence of bias, which are separate concerns. Explanation is a measure that helps stakeholders scrutinize and manage a system, not a control that removes underlying model risk.

Best practices

Define explainability objectives explicitly in terms of the intended audience and the decision the explanation supports, rather than adopting a generic requirement for 'explainable AI.'

Verify the actual normative status and scope of the specification against the published ISO/IEC text before citing it, since a technical specification is a voluntary standard and not law.

Maintain a clear internal distinction between explainability and interpretability in documentation and controls, so stakeholders do not assume one satisfies the other.

Treat explainability as one input to oversight and risk management, and pair it with separate assessments of fairness, bias, and model risk rather than assuming explanations address those concerns.

Tailor explanation formats and depth to each stakeholder group, recognizing that an explanation adequate for a developer may not meet the needs of an auditor or an affected individual.

Where the specification is used alongside other AI governance or model risk frameworks, map how it complements them without treating the specification as a substitute for jurisdiction-specific obligations.