Scaling of End-To-End Governance Risk Assessments for AI Systems (Practitioner Track)

Artificial Intelligence (AI) systems are embedded in a multifaceted environment characterized by intricate technical, legal, and organizational frameworks. To attain a comprehensive understanding of all AI-related risks, it is essential to evaluate both model-specific risks and those associated with the organizational and governance setups. We categorize these as "bottom-up risks" and "top-down risks," respectively. In this paper, we focus on the expansion and enhancement of a testing and auditing technology stack to identify and manage governance-related risks ("top-down"). These risks emerge from various dimensions, including internal development and decision-making processes, leadership structures, security…