AI Component Risk Aggregation for Granular Compliance Tracking
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Solution Overview
Problem
Conventional systems lack the ability to manage machine-learning models and datasets with granularity, leading to inefficiencies in tracking compliance with various protocols and inaccurately identifying risks associated with AI tools across multiple computing systems.
Innovation Solution
A risk aggregation system that generates data objects representing components of AI computing applications, administers risk assessments, and combines scores to provide an interactive aggregated risk indicator, enabling granular risk management and compliance tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems analyze AI tools at system level only, then the analysis covers the entire system, but the granularity of analysis is insufficient and compliance tracking is inaccurate
Solution Approach 1:
The patent divides the AI system into discrete components (machine-learning models, datasets, processing operations) and creates separate data objects for each. This segmentation enables granular risk assessment of individual components while maintaining system-level oversight through aggregation, resolving the contradiction between analysis precision and tracking complexity.
Solution Approach 2:
The patent combines granular component-level risk scores with system-level risk aggregation in a unified framework. Individual component risks are calculated separately and then merged into an overall system risk profile, enabling both detailed component tracking and comprehensive system analysis without the contradictions of either approach alone.
2Productivity
If conventional systems monitor risks at system level only, then the monitoring scope is comprehensive, but the ability to dynamically manage individual AI tools changes efficiently is lost
Solution Approach 1:
By segmenting the AI system into trackable component data objects, the patent enables efficient individual component management when changes occur. Each component can be assessed, modified, and re-assessed independently, improving productivity while maintaining component-level information through structured data objects that prevent information loss.
3Reliability
If conventional systems lack granular analysis capability, then the system is simpler to operate, but the accuracy of compliance tracking and risk management is insufficient
Solution Approach 1:
The system automatically generates data objects for AI components, performs risk assessments, and aggregates results without requiring manual intervention for each component. This self-service approach maintains ease of operation while achieving high reliability in compliance tracking through automated granular analysis and aggregation.
Data Source
AI summary
Methods, systems, and non-transitory computer readable storage media are disclosed for facilitating modification of components of artificial intelligence computing applications via aggregated risk scores of individual artificial intelligence computing application components. The disclosed system generates data objects representing the artificial intelligence computing application components. The disclosed system determines mappings between the data objects based on relationships of the artificial intelligence computing application components. Furthermore, the disclosed system generates risk scores for the data objects representing the components of the artificial intelligence computing application by administering risk assessments that correspond to a system requirements framework. The disclosed system also generates an interactive aggregated risk indicator indicating contributions of the components to the artificial intelligence computing application in relation to the system requirements framework by combining the risk scores of the data objects according to the mappings between the data objects.