Skip to content

4.2.1. What Role Do Standards Play in Structuring Trustworthy Data?

What Role Do Standards Play in Structuring Trustworthy Data?

“Without standards, governance is subjective. Without governance, data becomes a liability.”

The failures outlined in the previous section, ranging from hallucinated medical advice to untraceable data reuse, share a common thread: the absence of structural accountability in the dataset.

As AI development accelerates, many organizations face the same bottleneck: their datasets are large, but ungovernable. Too often, data is collected without structure, used without documentation, and reused without accountability. These early-stage decisions may seem minor, but they become critical failures when a system misbehaves or a regulator comes calling.

To address this challenge, the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) introduced a family of standards designed to bring order to this chaos: the ISO/IEC 5259 series.

This standard isn’t just about compliance, it’s a blueprint for making data describable, auditable, and governable by design.

"Without shared standards, fairness becomes philosophy. With them, it becomes engineering."
- Adapted from the goals of ISO/IEC TR 24027 and ISO/IEC TR 24028 on measurable AI trustworthiness

ISO/IEC 5259

The ISO/IEC 5259 series addresses this challenge by offering a standards-based framework to ensure that data used in AI and analytics systems is:

  • Describable: with detailed metadata and context
  • Traceable: through recorded lineage and transformation logs
  • Controllable: via versioning, licensing, and role-based access

Unlike ad hoc documentation practices, ISO/IEC 5259 formalizes these features into a repeatable and auditable data governance architecture.

Key Elements of ISO/IEC 5259-3 & 5259-51

While the full standard is broad in scope, several clauses are directly applicable to AI governance challenges:

  • ISO/IEC 5259-5, Clause 7 – Responsibilities of the Governing Body
    Defines organizational roles, responsibilities, and oversight structures. The governing body must set policies, allocate resources, and establish accountability mechanisms for managing data quality across the AI life cycle.

  • ISO/IEC 5259-3, Clause 6.1 – Objective
    Establishes the goal of creating appropriate (repeatable and auditable) processes to manage data quality and reliably meet organizational and stakeholder requirements.

  • ISO/IEC 5259-3, Clause 7.3.4.4 – Data Handling
    Requires processes to ensure traceability of data origin, modifications, and version control. This enables organizations to audit how data was obtained, altered, and used across training and deployment stages — essential for debugging and compliance.

  • ISO/IEC 5259-3, Clause 6.3 – Requirements and Recommendations
    Specifies the need to define data quality requirements, including dimensions such as representativeness, completeness, timeliness, and fitness-for-purpose, ensuring that datasets support trustworthy outcomes.

  • ISO/IEC 5259-3, Clause 7.2.4.2 (Validation and Verification) & Clause 7.3.7.4 (Tracking and Improvement)
    Mandates ongoing monitoring activities to detect issues like data drift, decay, and misalignment with intended use. These mechanisms ensure continuous assessment of data currentness and long-term quality.

⚠️ Did you know? Most open-source datasets used in AI (like Common Crawl or LAION-5B) do not meet ISO/IEC 5259 requirements for consent, metadata, or traceability.

These elements support not only ethical and legal compliance but also model robustness, reproducibility, and interpretability.

🔑 Key Takeaway: ISO/IEC 5259 in the Broader Governance Ecosystem

ISO/IEC 5259 does not operate in isolation. It complements a family of standards that together form a multi-layered scaffold for trustworthy AI:

  • ISO/IEC 24028:2020 – Trustworthiness of AI systems (risk, bias, robustness)[2]
  • ISO/IEC TR 24027:2021 – Bias and fairness assessment 3
  • ISO/IEC 38505-1:2017 – Governance of data for IT and business
  • ISO/IEC 27701:2019 – Privacy Information Management (extension to ISO/IEC 27001) 4
  • ISO/IEC 29100:2011 – Privacy framework: consent, purpose limitation, user rights 5

These link data-level traceability (via ISO/IEC 5259) with system-wide accountability, ensuring ethical, legal, and technical integrity across the AI lifecycle.

Translating Principles Into Practice

One of the unique contributions of ISO/IEC 5259 is that it bridges abstract ethical principles and concrete technical workflows.

Table 31: Ethical Principles Aligned with ISO/IEC 5259 Implementation

Ethical Principle ISO/IEC 5259 Implementation
Transparency Data lineage and metadata logs
Fairness Representation metrics and quality audits
Accountability Governance roles and continuous monitoring

This mapping turns ethics into practice, offering developers and policymakers a shared vocabulary for building trustworthy systems.

“Transparency refers to the ability to access and understand relevant information about a system, including the data used, the processes performed, and the results produced.”
— ISO/IEC TR 24028:2020, Clause 5.3.4

Challenges and Adoption

The standard is still relatively new (published in 2024), and adoption remains uneven:

  • Many open-source datasets (e.g., Common Crawl) do not meet ISO/IEC 5259 requirements
  • AI developers often lack internal tools for tracking metadata or lineage
  • In fast-paced environments, governance is treated as a bottleneck rather than an enabler

Yet signs of progress are emerging. Governments, multinational corporations, and cloud providers are beginning to incorporate 5259 into procurement, audits, and data infrastructure planning.

Toward Accountable Foundations

As AI becomes more powerful and pervasive, the cost of data failure rises. ISO/IEC 5259 doesn’t eliminate risk, but it offers a path toward measurable, verifiable, and improvable data practices.

When implemented properly, it turns datasets into assets that are not just large, but legitimate.

In the next section, we turn to a key component of this structure: metadata, not just as a backend log, but as a living layer of governance.

TRAI Challenge : ISO Standards for Dataset Governance

Read the following statements and mark them as True or False:

  1. ISO/IEC 5259-3 does not mandate synthetic data for privacy protection.
  2. Clause 7.3.4.4 requires traceability of data origin, modifications, and version control — i.e., dataset lineage.
  3. The standard establishes processes that make datasets auditable, traceable, and ethically manageable.
  4. Continuous quality monitoring, including drift detection, is addressed in Clause 7.2.4.2 (Validation and Verification) and Clause 7.3.7.4 (Tracking and Improvement), not Clause 7.2 alone.

🧩 Check the clause references and descriptions in 4.2.1.

Bibliography

  1. ISO/IEC. (2024). Artificial Intelligence , Data quality for analytics and machine learning , Part 3: Data quality management process. ISO/IEC FDIS 5259-3:2024(E). ↩

  2. ISO/IEC. (2020). Information technology , Artificial intelligence , Overview of trustworthiness in artificial intelligence. ISO/IEC 24028:2020. ↩

  3. ISO/IEC. (2021). Artificial intelligence , Bias in AI systems and AI-aided decision making. ISO/IEC TR 24027:2021. ↩

  4. ISO/IEC. (2019). Privacy information management , Extension to ISO/IEC 27001 and ISO/IEC 27002. ISO/IEC 27701:2019. ↩

  5. ISO/IEC. (2011). Information technology , Security techniques , Privacy framework. ISO/IEC 29100:2011. ↩