Overview
ISO/IEC TS 6254:2025 - "Information technology - Artificial intelligence - Objectives and approaches for explainability and interpretability of machine learning (ML) models and AI systems" is a Technical Specification that guides stakeholders on how to achieve explainability and interpretability across an AI system’s life cycle (per ISO/IEC 22989). It documents approaches, methods, and applicability of explainability measures to support transparent, auditable and user-centered AI behavior and outputs.
Key Topics
- Stakeholder objectives: Defines explainability goals for AI users, developers, product/service and platform providers, system integrators, data providers, evaluators, auditors, subjects and relevant authorities (policy makers, regulators).
- AI system life cycle: Guidance for integrating explainability during inception, design & development, verification & validation, deployment, operation & monitoring, continuous validation, re-evaluation and retirement.
- Property taxonomy of explainability methods: Characterizes explanation needs and properties including audience expertise, scope, completeness, depth, reasoning path, and implicit vs explicit explanations.
- Forms of explanation: Numeric, visual, textual, structured, example-based and interactive exploration tools for presenting explanations.
- Technical approaches:
- Empirical analysis methods (error analysis, fine-grained evaluation, ablation, analysis-oriented datasets)
- Post hoc interpretation methods (local and global explainability techniques)
- Inherently interpretable components (legible/meaningful models, models with explicit knowledge)
- Architecture- and task-driven methods (informative features, multi-step processing, rich inputs)
- Technical constraints and requirements: Considerations for method genericity, transparency requirements and display/presentation needs.
- Explainability evaluation: Methods for evaluating the explainability component and the role of explainability in overall verification and validation.
Applications
ISO/IEC TS 6254:2025 is practical for:
- AI developers and architects designing models with built-in interpretability or integrating post hoc explainers.
- Product and platform providers embedding explainability features into services and user interfaces.
- Regulators, auditors and evaluators assessing explanation sufficiency for compliance, safety and fairness.
- Policy makers and standards bodies shaping guidance for trustworthy AI and governance.
- Researchers and academia comparing methods and datasets for explainability research.
- End users and domain experts seeking appropriate explanation formats (visual, textual, example-based) to understand AI decisions.
Related Standards
- ISO/IEC 22989 - referenced for the AI system life cycle definition and interoperability between explainability guidance and lifecycle processes.
ISO/IEC TS 6254:2025 is a practical, stakeholder-focused resource for embedding explainability into ML and AI systems-useful for teams working on AI governance, transparent ML, model auditing and explainable AI (XAI) initiatives.