Documenting AI assessments under the EU AI Act involves a structured approach to recording your evaluation and compliance efforts related to AI systems, especially those the Act classifies as high-risk.
In fact, documentation demonstrates compliance with regulatory requirements, enhances transparency and accountability, and supports ongoing monitoring and risk management of AI deployments.
This post helps you document your AI assessments effectively.
Understanding the importance of documenting AI assessments
Documenting AI assessments is crucial for organisations deploying AI systems. It’s particularly crucial for those organisations deemed high-risk under the EU AI Act.
The documentation provides a comprehensive record of the system’s design, operation, and compliance with regulatory standards. This record, in turn, serves as evidence of due diligence and trustworthy AI use.
More specifically, the specific obligations for documentation, transparency, and reporting are mainly outlined in Chapter II of the Act. The Chapter deals with requirements for high-risk AI systems, especially Articles 10 through 15.
Key components of AI assessment documentation
- Comprehensive description of the AI system: Detailing the system’s purpose, data sources, algorithms, and decision-making logic is essential. Doing so helps demystify the AI system’s operations for regulators and stakeholders, clarifying its functionalities and output rationale.
- Risk assessment and classification: Documenting the process for determining the AI system’s risk level highlights the considerations for its classification as high-risk. For instance, it includes potential impacts on individuals’ rights and safety, underpinning the need for stringent compliance measures.
- Data governance and management practices: Outlining data handling practices ensures transparency in how data is sourced, processed, and protected. It emphasises efforts to maintain data integrity and address biases, which is crucial for the AI system’s ethical and fair operation.
- Technical and organisational measures: Describing implemented safeguards showcases the AI system’s commitment to security and reliability. You may want to include technical measures like encryption and organisational strategies like staff training and strict access controls.
- Human oversight: Detailing human oversight mechanisms underscores the balance between automated decisions and human judgment. It delineates roles in monitoring and potentially intervening in the AI system’s operations, ensuring accountability.
- Testing and validation: Providing evidence of rigorous testing and validation processes validates the AI system’s performance and adherence to ethical standards. It underscores the commitment to deploying safe and reliable AI solutions.
- Impact assessment: Detailing the AI system’s societal and individual impacts, both positive and negative, and the measures taken to amplify benefits while mitigating risks fosters trust among users and stakeholders.
- Compliance checklist: A summary of compliance efforts with the EU AI Act and related regulations is a quick reference to the organisation’s commitment to legal and ethical standards.
- Update and review log: Maintaining a log of updates and modifications to the AI system, including the rationale and impact on its risk level and compliance status, ensures transparency and accountability over time.
The significance of up-to-date documentation
Keeping AI assessment documentation current and accessible is pivotal for regulatory compliance and stakeholder trust. It demonstrates an ongoing commitment to responsible AI deployment, adapting to evolving standards and societal expectations.
