Blueprints of Trust: AI System Cards for End to End Transparency and Governance
This work addresses the need for improved governance and decision-making in AI development and deployment, though it appears incremental by building on existing model and system card concepts.
The paper tackles the problem of transparency and accountability in AI systems by introducing the Hazard-Aware System Card (HASC) framework, which integrates dynamic safety and security records with standardized identifiers like ASH IDs to provide a single source of truth for stakeholders.
This paper introduces the Hazard-Aware System Card (HASC), a novel framework designed to enhance transparency and accountability in the development and deployment of AI systems. The HASC builds upon existing model card and system card concepts by integrating a comprehensive, dynamic record of an AI system's security and safety posture. The framework proposes a standardized system of identifiers, including a novel AI Safety Hazard (ASH) ID, to complement existing security identifiers like CVEs, allowing for clear and consistent communication of fixed flaws. By providing a single, accessible source of truth, the HASC empowers developers and stakeholders to make more informed decisions about AI system safety throughout its lifecycle. Ultimately, we also compare our proposed AI system cards with the ISO/IEC 42001:2023 standard and discuss how they can be used to complement each other, providing greater transparency and accountability for AI systems.