SNZ TR ISO/IEC 24028:2025
Information technology – Artificial intelligence – Overview of trustworthiness in artificial intelligence
This technical report provides foundational guidance for understanding and improving the trustworthiness of AI systems across their lifecycle.
This technical report was considered for adoption by the P4830 Identical Adoption of Digital Standards Committee which included experts from government, industry, academia, and the private sector, all focused on the responsible use of emerging technologies.
This technical report is identical to and has been reproduced from ISO/IEC TR 24028:2020, Information technology – Artificial intelligence – Overview of trustworthiness in artificial intelligence.
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SNZ TR ISO/IEC 24028 2025
Committee representation
Acknowledgement
Copyright
Preface
Foreword
Introduction
1 Scope
2 Normative references
3 Terms and definitions
4 Overview
5 Existing frameworks applicable to trustworthiness
5.1 Background
5.2 Recognition of layers of trust
5.3 Application of software and data quality standards
5.4 Application of risk management
5.5 Hardware-assisted approaches
6 Stakeholders
6.1 General concepts
6.2 Types
6.3 Assets
6.4 Values
7 Recognition of high-level concerns
7.1 Responsibility, accountability and governance
7.2 Safety
8 Vulnerabilities, threats and challenges
8.1 General
8.2 AI specific security threats
8.2.1 General
8.2.2 Data poisoning
8.2.3 Adversarial attacks
8.2.4 Model stealing
8.2.5 Hardware-focused threats to confidentiality and integrity
8.3 AI specific privacy threats
8.3.1 General
8.3.2 Data acquisition
8.3.3 Data pre-processing and modelling
8.3.4 Model query
8.4 Bias
8.5 Unpredictability
8.6 Opaqueness
8.7 Challenges related to the specification of AI systems
8.8 Challenges related to the implementation of AI systems
8.8.1 Data acquisition and preparation
8.8.2 Modelling
8.8.3 Model updates
8.8.4 Software defects
8.9 Challenges related to the use of AI systems
8.9.1 Human-computer interaction (HCI) factors
8.9.2 Misapplication of AI systems that demonstrate realistic human behaviour
8.10 System hardware faults
9 Mitigation measures
9.1 General
9.2 Transparency
9.3 Explainability
9.3.1 General
9.3.2 Aims of explanation
9.3.3 Ex-ante vs ex-post explanation
9.3.4 Approaches to explainability
9.3.5 Modes of ex-post explanation
9.3.6 Levels of explainability
9.3.7 Evaluation of the explanations
9.4 Controllability
9.4.1 General
9.4.2 Human-in-the-loop control points
9.5 Strategies for reducing bias
9.6 Privacy
9.7 Reliability, resilience and robustness
9.8 Mitigating system hardware faults
9.9 Functional safety
9.10 Testing and evaluation
9.10.1 General
9.10.2 Software validation and verification methods
9.10.3 Robustness considerations
9.10.4 Privacy-related considerations
9.10.5 System predictability considerations
9.11 Use and applicability
9.11.1 Compliance
9.11.2 Managing expectations
9.11.3 Product labelling
9.11.4 Cognitive science research
10 Conclusions
Annex A (informative) Related work on societal issues
Bibliography
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SNZ TR ISO/IEC 24028 2025
Committee representation
Acknowledgement
Copyright
Preface
Foreword
Introduction
1 Scope
2 Normative references
3 Terms and definitions
4 Overview
5 Existing frameworks applicable to trustworthiness
5.1 Background
5.2 Recognition of layers of trust
5.3 Application of software and data quality standards
5.4 Application of risk management
5.5 Hardware-assisted approaches
6 Stakeholders
6.1 General concepts
6.2 Types
6.3 Assets
6.4 Values
7 Recognition of high-level concerns
7.1 Responsibility, accountability and governance
7.2 Safety
8 Vulnerabilities, threats and challenges
8.1 General
8.2 AI specific security threats
8.2.1 General
8.2.2 Data poisoning
8.2.3 Adversarial attacks
8.2.4 Model stealing
8.2.5 Hardware-focused threats to confidentiality and integrity
8.3 AI specific privacy threats
8.3.1 General
8.3.2 Data acquisition
8.3.3 Data pre-processing and modelling
8.3.4 Model query
8.4 Bias
8.5 Unpredictability
8.6 Opaqueness
8.7 Challenges related to the specification of AI systems
8.8 Challenges related to the implementation of AI systems
8.8.1 Data acquisition and preparation
8.8.2 Modelling
8.8.3 Model updates
8.8.4 Software defects
8.9 Challenges related to the use of AI systems
8.9.1 Human-computer interaction (HCI) factors
8.9.2 Misapplication of AI systems that demonstrate realistic human behaviour
8.10 System hardware faults
9 Mitigation measures
9.1 General
9.2 Transparency
9.3 Explainability
9.3.1 General
9.3.2 Aims of explanation
9.3.3 Ex-ante vs ex-post explanation
9.3.4 Approaches to explainability
9.3.5 Modes of ex-post explanation
9.3.6 Levels of explainability
9.3.7 Evaluation of the explanations
9.4 Controllability
9.4.1 General
9.4.2 Human-in-the-loop control points
9.5 Strategies for reducing bias
9.6 Privacy
9.7 Reliability, resilience and robustness
9.8 Mitigating system hardware faults
9.9 Functional safety
9.10 Testing and evaluation
9.10.1 General
9.10.2 Software validation and verification methods
9.10.3 Robustness considerations
9.10.4 Privacy-related considerations
9.10.5 System predictability considerations
9.11 Use and applicability
9.11.1 Compliance
9.11.2 Managing expectations
9.11.3 Product labelling
9.11.4 Cognitive science research
10 Conclusions
Annex A (informative) Related work on societal issues
Bibliography
SNZ TR ISO/IEC 24028:2025
Online Library subscription information
Online library subscriptions provide a cost-effective solution for organisations of any size to access the latest standards, offering 24/7 access to a personalised catalogue. The service ensures subscribers have have the most up-to-date version of selected standards, incluing automatic updates for any amendments or versions. Pricing is dependent on the chosen standards and the number of concurrent users, with discounts applied from the standards PDF price. For customised subscriptions or further inquiries, contact the Online Library team.