Machine Learning Bill of Materials (ML-BOM)

Introduction to ML-BOM

CycloneDX facilitates transparency in AI and machine learning systems by representing critical information about models, datasets, and their dependencies. This includes the provenance of datasets, training methodologies, and the configuration of AI frameworks. Such detailed visibility allows organizations to assess risks related to bias, data integrity, or model security, ensuring AI systems align with ethical and regulatory standards.

As AI adoption grows, so does the importance of accountability. CycloneDX empowers organizations to document and analyze their AI systems comprehensively, enabling informed decisions about their deployment and maintenance. By integrating with other system inventories, it ensures AI components are part of a unified approach to system transparency and risk management.

Highlights

• Represents datasets, models, and configurations for AI and machine learning systems.
• Documents provenance and ethical considerations for datasets.
• Supports transparency in AI decision-making processes.
• Identifies risks related to bias, data integrity, and model security.

Expected Outcomes

• Improved accountability and trust in AI systems.
• Greater compliance with ethical and regulatory standards.
• Enhanced ability to identify and mitigate risks in AI deployments.
• Unified integration of AI systems with other transparency efforts.