Complying with the EU AI Act

As AI systems become increasingly integrated into business operations, ensuring their ethical use and compliance with regulations is crucial. The European Union’s (EU) AI Act is the first comprehensive legal framework to regulate AI. An article by Exterro says that determining the risk category for an organization’s AI systems is the initial step toward compliance with the EU AI Act.

Unacceptable risk systems, which pose significant threats to safety or rights, are prohibited by the EU AI Act. High-risk systems, used in critical areas such as healthcare, transportation, and law enforcement, require stringent controls and oversight. Limited risk systems have transparency obligations but are subject to fewer regulations. Minimal risk systems are largely exempt from regulations.

To align with the EU AI Act, the article offers these five recommendations:

Establish a robust risk management system. Catalog all AI systems in use and classify them according to the EU AI Act’s risk categories. Create a risk management framework with ongoing monitoring using metrics and benchmarks to ensure compliance. Legal, IT, and business units should all be involved in the risk assessment process.

Ensure data governance and quality, particularly for high-risk systems. Ensure that data is accurate, relevant, and unbiased. Maintain detailed records of data sources and processing methods to demonstrate compliance. Adhere to data protection laws and establish robust data security protocols.

Develop comprehensive technical documentation. This should cover system design, development, and performance metrics for high-risk AI systems. Document every stage of the AI lifecycle to ensure transparency.

Focus on accuracy, robustness, and cybersecurity. Regularly test AI systems to validate their performance and address discrepancies or biases. Design AI systems to withstand cyberattacks, implement comprehensive cybersecurity measures, and conduct regular security audits.

Finally, establish a quality management system (QMS). Create a framework with policies and best practices for AI development and deployment. Stay updated on compliance requirements by working with regulatory bodies and connecting with peers. Regularly audit AI systems and processes to ensure ongoing compliance and identify areas for improvement.