System-Level RAI Simulation

Summary: System-level RAI simulation is a cost-effective way to comprehend the characteristics and behaviors of AI systems and to assess potential RAI risks before deploying them in the real world.

Type of pattern: Process pattern

Type of objective: Trustworthiness

Target users: Architects

Impacted stakeholders: Developers, data scientists

Lifecycle stages: Design

Relevant AI ethics principles: Human, societal and environmental wellbeing, human-centered values, fairness, privacy protection and security, reliability and safety, transparency and explainability, contestability, accountability

Mapping to AI regulations/standards: EU AI Act, ISO/IEC 42001:2023 Standard.

Context: AI is becoming increasingly prevalent and significant in our society. Many software systems have integrated AI for identifying patterns in data and making data-driven decisions in an autonomous and potentially opaque manner. This autonomous and opaque decision-making brings a high-level of uncertainty in the behavior and can result in serious RAI risks and unintended consequences. To avoid ethical disasters and gain public trust, it is essential to have a thorough understanding of the characteristics and behaviors of AI systems.

Problem: What are ways to understand the characteristics and behaviors of AI systems to prevent severe and unnecessary consequences?

Solution: System-level RAI simulation is a cost-effective way to assess the behaviors of AI systems before deploying them in the real world. A simulation model needs to be constructed in a way that mimics the potential behaviors and decisions of the AI system and assesses the ethical impacts. The assessment results can then be shared with the development team or potential users before the AI systems are deployed in the real world.


  • Reduced ethical risks: Simulating AI systems at system-level can uncover potential RAI risks and enhance the ethical quality of AI systems.
  • Cost efficiency: System-level simulation can anticipate potential RAI risks and prevent severe ethical disasters before deploying AI systems in the real-world.


  • Limited accuracy: The simulation model is based on the assumptions and data used to build the simulation model. These assumptions may not reflect real-world scenarios fully, leading to limited prediction capability.
  • Lack of scalability: Simulation models are often designed for a specific use case, thus they may not be easily adaptable to other scenarios.

Related patterns:

  • RAI digital twin: An RAI digital twin performs system-level simulation in real-time using live data. The assessment results are fed back to alert the system or user before unethical behavior or decision takes effect.

Known uses: