Lack of system transparency

Mitigation strategy

1. Establish a Mandated, Comprehensive Documentation Framework Implement a formal policy requiring the systematic and continuous documentation of every AI system. This documentation must include the model's design specifications, the provenance and characteristics of the training data, the specific intended purpose and scope of the model within its operational system, and detailed, auditable records of all configuration and deployment decisions. This ensures the system's inner workings are intelligible and not a "black box" (Source 3, 16). 2. Integrate Transparency Requirements into AI Governance and Audit Protocols Embed the principle of transparency as a core requirement within the overarching AI governance framework. This involves establishing clear accountability for documentation quality, mandating the use of interpretable machine learning techniques where feasible, and requiring regular, independent audits of the AI system to verify that its documentation accurately reflects its operational behavior and aligns with the stated purpose and ethical values (Source 15, 16, 17). 3. Enforce Stakeholder-Centric Transparency and Communication Protocol Develop and execute a strategic communication plan that articulates the AI model's specific role, its operational boundaries, and its decision logic to relevant internal and external stakeholders. This ensures a shared organizational understanding of the model's purpose, facilitates scrutiny, and builds trust by proactively addressing the governance risk of a lack of clarity regarding the model's function within the broader system (Source 1, 4, 10, 20).