How should providers establish and maintain the risk management system required by Article 9, and what are the key elements to consider?

Establishing and Maintaining a Risk Management System (Article 9)

Article 9 mandates a continuous, iterative risk management system for high-risk AI systems. This isn't a one-time task; it's an ongoing process throughout the AI system's lifecycle. Here's a practical approach:

1. Initial Setup and Documentation (Article 9(1), 9(2))

• Establish a System: Create a formal risk management system. This could be integrated into existing risk management frameworks (e.g., ISO 31000) if you have them, but it must address the specific requirements of the AI Act. Recital 81 and Article 17 discuss integration with quality management systems.
• Document the System: Thoroughly document the system's policies, procedures, and responsibilities. This documentation is crucial for demonstrating compliance (Article 9(1)). This should be part of the technical documentation required under Article 11. Specifically, Article 11(1)(g) requires the inclusion of the risk management system.
• Iterative Process: Explicitly design the system as a continuous, iterative process . This means planning for regular reviews, updates, and improvements (Article 9(2)).
• Lifecycle Approach: The system must cover the entire lifecycle of the AI system, from initial design and development to deployment and decommissioning (Article 9(2)).

2. Risk Identification and Analysis (Article 9(2)(a))

• Intended Purpose: Identify and analyze risks related to the AI system's intended purpose (Article 9(2)(a)). This is the starting point. Consider:
  • What is the system designed to do?
  • What are the intended outputs and outcomes?
  • Who are the intended users and those affected?
• Known and Foreseeable Risks: Identify both known risks (based on existing knowledge and experience) and reasonably foreseeable risks (Article 9(2)(a)). This requires proactive thinking and research. Consider:
  • What could go wrong, even if the system functions as intended?
  • What are the potential harms to health, safety, and fundamental rights? Be specific (e.g., discrimination, bias, privacy violations, safety hazards).
• Systematic Approach: Use a systematic approach to risk identification. This could involve:
  • Brainstorming: With a diverse team of experts (technical, legal, ethical).
  • Checklists: Based on known AI risks and relevant standards.
  • Failure Mode and Effects Analysis (FMEA): A structured method for identifying potential failures and their consequences.
  • Hazard and Operability Studies (HAZOP): Another structured method, often used in engineering.
  • Review of Literature and Incident Databases: Learn from past incidents and research.

3. Risk Estimation and Evaluation (Article 9(2)(b))

• Reasonably Foreseeable Misuse: Estimate and evaluate risks arising from reasonably foreseeable misuse (Article 9(2)(b)). This is crucial and often overlooked. Consider:
  • How could the system be used in ways it wasn't intended?
  • Could it be used maliciously?
  • Could users misunderstand or misinterpret the system's outputs?
• Severity and Probability: For each identified risk (both intended use and misuse), assess:
  • Severity: How serious would the harm be? (e.g., minor inconvenience, significant financial loss, physical injury, discrimination).
  • Probability: How likely is the harm to occur? (e.g., rare, unlikely, possible, likely, almost certain).
• Risk Matrix: Use a risk matrix (or similar tool) to combine severity and probability into an overall risk level (e.g., low, medium, high, critical). This helps prioritize risks.

4. Post-Market Monitoring and Risk Re-evaluation (Article 9(2)(c), Article 72)