As artificial intelligence (AI) systems become increasingly embedded in business, government, healthcare, manufacturing, and testing environments, the need for confidence in AI-driven decisions has never been greater. Many of these systems use machine learning techniques to improve efficiency, identify patterns, automate processes, and support decision-making. However, with these opportunities come new challenges around transparency, reliability, bias, governance, and trust, especially for safety- and mission-critical applications.
This is where accreditation has an increasingly important role to play.
What are AI Systems and Machine Learning?
AI systems are technologies designed to perform tasks that usually require human intelligence, such as recognising patterns, interpreting information, making predictions, or supporting decisions. Machine learning is a family of techniques that enables these systems to learn from data and improve their performance over time without being explicitly programmed for every scenario. Applications range from predictive maintenance and medical diagnostics to fraud detection, laboratory automation, image recognition, and document analysis.
Unlike traditional software, AI systems that incorporate machine learning can evolve as they are exposed to new data. Over time, such AI systems can generate different outputs form the same input While this flexibility offers significant benefits, it also introduces risks that must be effectively managed, validated, and monitored.
Why Trust Matters
Decisions influenced by AI systems can have significant impacts on businesses, consumers, and society. Questions often arise about:
- Accuracy and reliability of results
- Data quality and integrity
- Algorithm bias and fairness
- Transparency of decision-making
- Cybersecurity and privacy
- Ongoing monitoring and governance
Without appropriate oversight, organisations may struggle to demonstrate that their AI systems are operating safely, ethically, and effectively. As decision-makers increasingly rely on automated systems, the need for human oversight, expert judgement, and independent assurance becomes even more important.
The Emerging Role of Accreditation
Accreditation has long provided confidence in laboratories, inspection bodies, certification bodies, and conformity assessment activities. As AI systems become more widespread, and decision-makers increasingly rely on automated systems, the importance of human oversight, expertise and judgement continues to grow. NATA’s peer-review model of accreditation provides exactly that, placing experienced professionals at the centre of the assessment process.
The principles that underpin accreditation, competence, impartiality, traceability, and consistency, are becoming equally important in the AI landscape.
Accreditation can support confidence in the responsible use of AI by:
- Verifying the competence of organisations assessing AI systems
- Providing confidence in testing and validation activities
- Supporting consistent application of international standards
- Demonstrating independent oversight
- Promoting transparency and accountability
Just as accreditation builds trust in laboratory test results, it can help build trust in AI-supported outcomes by combining technical assessment with expert human judgement. NATA’s peer-review model is particularly relevant in this context, because it draws on the expertise of people who understand the systems, risks, and real-world environments in which conformity assessment operates.
A New Framework for AI Assurance
A significant development in this area is the publication of ISO/IEC 42001, the world’s first Artificial Intelligence Management System standard. The standard provides organisations with a framework for establishing, implementing, maintaining, and continually improving the governance of AI systems.
The standard addresses key issues such as:
- Risk management
- Transparency
- Accountability
- Ethical considerations
- Continuous improvement
- Governance of AI systems
Accredited certification against ISO/IEC 42001, ISO/IEC 23894:2023, ISO/IEC 2305:20233 and ISO/IEC 38507:2022 enables organisations to demonstrate responsible management of AI technologies and provides stakeholders with greater confidence in how AI systems are developed, governed, and used. In Australia and New Zealand, certification bodies operating in this space may themselves be accredited by JASANZ.
Opportunities for the Accreditation Community
For accreditation bodies, conformity assessment providers, and technical experts, AI systems present both challenges and opportunities.
Accredited organisations are already exploring how AI can enhance testing, inspection, certification, and quality management activities. AI-assisted document review, predictive analytics, anomaly detection, and automated data analysis are becoming increasingly common.
At the same time, accreditation bodies must ensure these technologies are applied appropriately and that any AI-supported processes remain robust, transparent, and technically valid.
The broader quality infrastructure community is also recognising the importance of AI assurance. Initiatives such as the Walbrook AI Accord have highlighted the need for international standards, assurance frameworks, and accredited conformity assessment activities to support the responsible deployment of AI technologies.
Looking ahead
AI systems are transforming industries at an unprecedented pace. While the technology offers enormous potential, its success ultimately depends on trust. Organisations, regulators, and consumers need confidence that AI systems are accurate, reliable, ethical, and governed appropriately.
Accreditation is uniquely positioned to provide that confidence.
By applying established principles of competence, impartiality, independent assessment, and peer review to emerging AI technologies, the accreditation community can help ensure that AI delivers innovation without compromising trust.
As AI continues to evolve, accreditation will play a critical role in bridging the gap between technological advancement and public confidence, helping organisations harness the benefits of AI while maintaining the highest standards of quality, assurance, and responsible use.