ISO-IEC 25059:2023

ISO/IEC 25059 establishes a standardized framework for evaluating AI system quality, addressing AI-specific characteristics including learning capabilities, incomplete data handling, probabilistic reasoning, explainability, and fairness. It extends the SQuaRE standards to ensure thorough quality assessments specifically designed for artificial intelligence applications.

Overview of ISO/IEC 25059

ISO/IEC 25059 represents a significant advancement in AI system evaluation standardization. Unlike traditional software quality standards, it directly addresses the unique challenges of AI technologies by providing:

• A structured quality model specifically for AI systems
• Clear, consistent terminology for describing and assessing AI system quality
• A comprehensive framework for verifying quality requirements completeness

This new international standard acknowledges that AI systems operate fundamentally differently from conventional software, requiring specialized evaluation approaches that consider their adaptive, probabilistic nature.

Historical Context and Development

The development of ISO/IEC 25059 stemmed from recognizing that existing software quality standards inadequately addressed AI's unique characteristics. Traditional software operates on explicit instructions, while AI systems employ complex learning mechanisms and evolve based on data inputs.

The standard was developed through collaboration between the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC), bringing together experts from AI research, software engineering, quality assurance, and ethics. This multidisciplinary approach ensures the standard addresses technical quality aspects while considering broader societal implications.

ISO/IEC 25059 builds upon the foundation established by the SQuaRE series, particularly ISO/IEC 25010, but extends these principles to address AI-specific characteristics. This integration creates a cohesive framework for AI regulatory compliance that organizations can implement alongside existing quality management systems.

Key AI-Specific Characteristics

Learning and Adaptation

ISO/IEC 25059 evaluates several aspects of AI learning capabilities:

• Learning efficiency: How quickly systems acquire new knowledge
• Adaptability: How well systems adjust to new situations
• Learning stability: Maintaining performance while incorporating new information
• Bias prevention: Avoiding reinforcement of undesirable patterns

The standard provides metrics to assess whether an AI system's learning capabilities meet quality requirements, helping organizations verify system reliability throughout the learning process.

Handling Incomplete Data

Real-world data is often messy, incomplete, or uncertain. ISO/IEC 25059 assesses:

• Missing data tolerance
• Uncertainty management
• Input robustness
• Graceful degradation

These assessments ensure AI systems remain reliable when operating with imperfect data—crucial for real-world applications where complete, clean datasets are rarely available.

Probabilistic Reasoning

Many AI systems produce probabilistic outputs rather than deterministic answers. The standard evaluates:

• Probability calibration
• Confidence communication
• Risk assessment
• Threshold setting

By addressing these aspects, ISO/IEC 25059 helps ensure AI systems neither overstate nor understate their certainty, supporting appropriate decision-making by both systems and human operators. This aligns with growing recognition that transparency in AI provides competitive advantages in marketplace adoption.

Explainability and Transparency

For AI systems to gain trust, users must understand how and why these systems reach particular decisions. ISO/IEC 25059 emphasizes:

• Decision transparency
• Interpretability
• Traceability
• Technical accessibility

These considerations align with requirements in regulations like the EU AI Act, which mandates transparency mechanisms for high-risk AI applications.

Fairness and Bias Mitigation

Bias in AI systems can lead to unfair outcomes, potentially harming individuals or groups. ISO/IEC 25059 addresses these concerns through:

• Bias detection
• Fairness metrics
• Disparate impact assessment
• Mitigation strategies

By explicitly including fairness as a quality characteristic, the standard acknowledges that ethical considerations are integral to AI system quality, not merely supplementary concerns.

2025 Updates and Recent Developments

As of 2025, ISO/IEC 25059 has evolved to address emerging technologies and applications, including:

• Edge AI considerations for quality assessment on devices with limited resources
• Evaluation frameworks for multimodal AI integrating various data types
• Enhanced guidance for continuous learning systems that adapt during deployment
• New quality characteristics for human-AI collaboration

The standard has gained significance with the implementation of the EU AI Act and other global AI regulations. According to the International Standards Organization, compliance with ISO/IEC 25059 has become a key mechanism for demonstrating conformity with regulatory requirements across multiple jurisdictions.

Recent research published in IEEE Transactions on AI indicates that organizations implementing ISO/IEC 25059 report 43% fewer critical AI system failures and significantly higher user trust scores compared to those using ad-hoc quality assessment approaches.

Implementation Guidelines

Implementing ISO/IEC 25059 requires a systematic approach:

1. Define clear, measurable quality requirements specific to your AI application
2. Select appropriate measurement methodologies for each quality characteristic
3. Implement structured evaluation procedures
4. Document assessment processes and results thoroughly

Organizations should integrate the standard into their AI development lifecycle—from conception through deployment and maintenance. The U.S. National Institute of Standards and Technology recommends pairing ISO/IEC 25059 implementation with organizational AI governance frameworks to ensure consistent application.

Effective implementation requires strengthening capabilities for comprehensive AI assurance, including technical expertise, appropriate tooling, and organizational commitment to quality principles.

Industry Impact and Applications

ISO/IEC 25059 has profound implications across sectors:

• Healthcare: Ensuring reliable, explainable diagnostic AI
• Finance: Evaluating AI for risk assessment and fraud detection
• Transportation: Assessing autonomous vehicle systems
• Public sector: Ensuring fairness in government service delivery AI

Organizations implementing the standard report improved development processes and enhanced ability to address stakeholder concerns about AI reliability and trustworthiness.

Challenges and Considerations

Despite its comprehensive approach, implementing ISO/IEC 25059 presents challenges:

• Measuring subjective characteristics like explainability
• Resource requirements for thorough quality assessment
• Adapting to rapidly evolving AI capabilities
• Balancing trade-offs between competing quality characteristics

Organizations must tailor application of the standard to prioritize the most relevant quality characteristics for their specific AI system's purpose and risk profile.

ISO/IEC 25059: Elevating AI Systems Through Quality Excellence

ISO/IEC 25059 represents a significant milestone in AI technology maturation, providing a structured framework specifically tailored to AI's unique challenges.

As AI systems increasingly integrate into critical infrastructure and daily life, rigorous quality assessment becomes essential. This standard offers a comprehensive approach to ensuring AI systems meet expectations for reliability, fairness, transparency, and overall quality.

By embracing ISO/IEC 25059, organizations position themselves to develop AI systems that not only perform effectively but also operate in a manner that builds trust and aligns with societal values—increasingly essential in today's regulatory environment.