An effective AI risk management framework encompasses identifying, assessing, mitigating, and continuously monitoring risks associated with AI systems. Organizations either adopt a specific framework or integrate AI risk management into existing privacy frameworks to mitigate AI-related vulnerabilities and threats. The OECD’s High-Level AI Risk Management Interoperability Framework highlights four critical steps:
Defining: Identify the AI system’s purpose, data flow, processing mechanisms, relevant actors, and compliance obligations. This stage also involves determining the risk model based on jurisdiction and business type, with AI impact assessments aiding in this process.
Risk Assessment: Evaluate and score the risks (high, low, medium) based on potential personal data violations and other factors like adverse impacts on individuals. High-risk AI systems may threaten safety, livelihoods, or fundamental rights, or process sensitive data, including children’s data. Conversely, AI applications with no adverse individual impacts, such as certain video games, are considered low-risk.
Risk Treatment:
Risk Response Determination: Decide on the response to manage identified risks, which may involve transferring the risk or applying controls to mitigate it.
Risk Control Implementation: Implement controls (administrative, technical, or physical) to reduce risk, such as protocols to address biases and tools to ensure fairness.
Monitoring and Reviewing: Continuously monitor AI systems as they and associated risks may evolve. This includes maintaining logs of data processing, informing individuals of data use changes, and periodically cleaning obsolete data to uphold data accuracy and compliance with privacy principles.
