Data quality in AI systems: what the AI Act requires
Among the requirements for high-risk AI systems, data quality represents a fundamental element. The regulation establishes that datasets must meet specific quality standards to ensure reliability and prevent bias.
What the regulation requires
The AI Act establishes that datasets used in high-risk systems must meet certain quality standards.
Specifically, they must be:
relevant to the purpose of the system
representative of the context in which they will be used
accurate and as error-free as possible
complete, to avoid distortions in results.
This implies structured work of data collection, selection and verification.
The problem of bias
One of the most delicate aspects concerns the presence of bias in data.
If datasets reflect imbalances or discrimination already present in reality, the AI system risks amplifying them.
For this reason, organizations must adopt measures to identify and mitigate any distortions.
Conclusion
Ensuring data quality is not just a technical requirement, but a fundamental element to ensure the reliability of AI systems.
For many companies, this means introducing new data governance processes and more rigorous controls throughout the development cycle.