AI Model BOM Generation for Transparent Training Audits
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for generating a bill of materials (BOM) fail to meet the unique requirements of artificial intelligence (AI) models, particularly in ensuring transparent auditing, risk management, and consistency verification during training, testing, deployment, and running, due to the complexity and multi-party involvement in AI model development.
Innovation Solution
A method and device for generating an AI model BOM that includes training dependency, model composition, and metadata information, enabling consistency verification, transparent auditing, and risk management by capturing details such as dataset, pre-trained models, initialization parameters, and intermediate models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional software BOM is reused for AI models, then implementation simplicity is improved, but transparency and auditability requirements cannot be met
Solution Approach 1:
The patent segments the BOM into distinct components: model metadata (identification, version, creator), training dependency information (datasets, pre-trained models, parameters, scripts), and model composition information (intermediate models, architecture, layers). This segmentation enables specialized tracking of each component's provenance and characteristics, providing the transparency needed for AI model auditing while maintaining a structured implementation approach.
Solution Approach 2:
The patent introduces a bill of materials generator as an intermediary system that automatically collects, validates, and organizes information about AI model components. This intermediary tool bridges the gap between simple implementation and comprehensive auditing by automating the capture of metadata, training dependencies, and composition details, thereby enabling transparent auditing without significantly increasing implementation complexity.
2Reliability
If detailed training dependency information is captured, then transparent auditing is improved, but information management complexity increases
Solution Approach 1:
The patent designs the bill of materials structure to serve multiple functions simultaneously: it tracks provenance of training data, records model architecture details, monitors training parameter changes, and enables audit trails. By making the BOM multi-functional, the system achieves comprehensive transparent auditing without proportionally increasing management complexity, as a single structured framework handles multiple auditing requirements.
Solution Approach 2:
The patent incorporates verification mechanisms that provide feedback loops for validating captured information. The bill of materials generator verifies the consistency and validity of recorded metadata, training dependencies, and composition information. This feedback mechanism ensures data quality and consistency, reducing the operational complexity of managing detailed information by automatically detecting and correcting anomalies.
3Adaptability or versatility
If multi-party cooperation is implemented in AI model development, then model capability is improved, but consistency verification becomes difficult
Solution Approach 1:
The patent implements preliminary actions by capturing and recording critical information about AI model components at the time of their creation or introduction. The bill of materials generator collects metadata, training dependency information, and composition details before these components are integrated into the final model. This preliminary documentation ensures that all contributions from multiple parties are properly recorded and can be verified for consistency later in the development lifecycle.
Solution Approach 2:
The patent establishes feedback mechanisms that continuously verify consistency across multi-party contributions. The bill of materials structure enables cross-checking of information from different parties (data providers, model creators, training systems) to ensure they align with declared characteristics and provenance. This feedback verification maintains consistency in collaborative environments without hindering model capability development.
Data Source
Figure 1~2
Figure 3a~3b
Figure 4~5
AI summary
Embodiments of this application disclose a method for generating a bill of materials file and a related device. The method includes: A generation device obtains target information, where the target information includes training dependency information, model composition information, and model metadata, the training dependency information is information about a training resource for training an AI model, the model composition information is information about an intermediate model in a process of training the AI model, and the model metadata is attribute information of the AI model. The generation device generates a bill of materials file of the AI model, where the bill of materials file includes the target information.