07.4 Quality of data for AI systems

The organisation shall define and document requirements for data quality and ensure that data used to develop and operate the AI system meet those requirements. This control is essential for maintaining governance and ensuring that AI systems are managed responsibly. It should be applied consistently across all relevant organisational processes and reviewed periodically to remain effective.

Business Requirement The purpose of this control is to safeguard organisational integrity, compliance with legal and regulatory requirements, and to promote trustworthiness in AI systems. It ensures that risks are mitigated and that the organisation’s objectives for responsible AI use are achieved.

The quality of data used to develop and operate AI systems potentially has significant impacts on the validity of the system’s outputs. ISO/IEC 25024 defines data quality as the degree to which the characteristics of data satisfy stated and implied needs when used under specified conditions. For AI systems that use supervised or semi-supervised machine learning, it is important that the quality of training, validation, test and production data are defined, measured and improved to the extent possible, and the organisation should ensure that the data are suitable for its intended purpose. The organisation should consider the impact of bias on system performance and system fairness and make such adjustments as necessary to the model and data used to improve performance and fairness so they are acceptable for the use case. Organisations should implement this control by establishing clear procedures, assigning responsibilities, and maintaining accurate documentation. Practical steps include integrating this control into existing governance frameworks, training relevant personnel, and monitoring compliance through regular audits.