What is a key requirement for data quality under the EU AI Act?

A key requirement for data quality under the EU AI Act is bias mitigation. The Act emphasizes the importance of having high-quality data used in AI systems, as poor data quality can lead to biased outcomes, which can further perpetuate discrimination and inequality. By addressing bias in the data, organizations can ensure that AI systems are fair and trustworthy, thereby enhancing the overall reliability of AI technologies. This focus on bias mitigation is crucial, as it directly aligns with the broader goals of the EU AI Act to promote ethical AI practices and protect fundamental rights.

Regular updates, transparency, and accountability are also important aspects of AI governance, but they serve different purposes within the framework. Regular updates relate to keeping systems current and responsive. Transparency concerns how AI operations are communicated to users and stakeholders. Accountability focuses on the responsibility for outcomes resulting from AI systems. While these elements contribute to the overarching aim of ensuring ethical and effective AI, they do not specifically address the vital requirement of ensuring that the data used in AI systems is free from biases, which is a direct factor affecting data quality as outlined in the EU AI Act.