Dynamic Fact Contextualization for AI Model Governance
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Solution Overview
Problem
Fact producers in AI model development lack visibility into the current state of fact collection and documentation, leading to challenges in recording and validating facts for AI governance, particularly for non-automated facts that require human composition.
Innovation Solution
A computer-implemented method for dynamic fact contextualization, which involves selecting a template with definitions for identifying facts, retrieving valid facts from a repository, using a machine learning model to identify deficient facts, and updating the FactSheet with corrected facts, providing a preview to guide fact producers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If facts are gathered throughout the AI lifecycle without real-time contextualization, then fact collection is flexible and continuous, but fact producers lack visibility into the current state of fact collection and documentation
Solution Approach 1:
The system provides real-time feedback to fact producers by displaying the current state of fact collection and documentation through a preview interface. This feedback mechanism shows what facts have been collected, what is missing, and how the gathered facts relate to the eventual documentation, enabling fact producers to understand the current state without adding significant system complexity.
Solution Approach 2:
The system performs preliminary actions by pre-defining templates with fact definitions and policies before fact collection begins. This allows the system to automatically validate and contextualize facts as they are entered, providing visibility into the collection state without requiring complex real-time processing during fact entry.
2Ease of operation
If non-automated facts require human composition without guidance, then fact creation is flexible, but fact producers do not know what to record and how to record it
Solution Approach 1:
The system prepares templates with predefined fact definitions and validation policies before fact collection begins. These templates guide fact producers on what to record and how to record it by presenting structured forms and validation rules, reducing the time needed to understand requirements while maintaining flexibility in fact creation.
Solution Approach 2:
The system provides real-time feedback to fact producers during the fact creation process by validating entered facts against predefined policies and showing what information is required. This guidance mechanism helps producers understand what to record and how to record it without adding significant complexity to the operation.
3Reliability
If facts are collected without automated validation, then fact collection is simple and continuous, but facts do not consistently meet policy requirements
Solution Approach 1:
The system pre-establishes validation policies and templates before fact collection begins. These predefined rules automatically validate facts as they are entered, ensuring consistency with policy requirements without requiring complex validation logic during collection. The preliminary setup enables reliable fact validation while maintaining high collection efficiency.
Solution Approach 2:
The system performs self-service validation by automatically checking entered facts against predefined policies without requiring manual review. This automated validation ensures fact reliability and consistency while maintaining continuous and efficient fact collection, as the system validates facts in real-time without adding significant operational complexity.
4Adaptability or versatility
If fact producers work independently without visibility into overall documentation state, then work is flexible and autonomous, but coordination across multiple users is difficult
Solution Approach 1:
The system provides real-time feedback to all users about the current state of fact collection and documentation progress. This feedback mechanism displays what facts have been collected, what is missing, and how individual contributions relate to the overall documentation, enabling better coordination across multiple users while maintaining flexible independent work.
Solution Approach 2:
The system serves multiple functions simultaneously: it collects facts, validates them against policies, provides real-time visibility into collection state, and enables coordination among users. This multi-functionality is achieved through a unified template-based system that handles all these aspects without requiring separate complex systems for each function.
Data Source
AI summary
Provided are techniques for dynamic fact contextualization in support of AI model development. A template from a plurality of templates is selected, where the template includes definitions for identifying facts. The facts are retrieved from a facts repository based on the definitions. It is determined that that the facts are valid based on one or more policies. A FactSheet is generated using the template and the facts. A machine learning model is used to identify one or more deficient facts from the FactSheet. The FactSheet is displayed in a preview with the one or more deficient facts. One or more facts corresponding to the one or more deficient facts are located. The FactSheet is updated to correct the one or more deficient facts with the corresponding facts.