Automated AI Model Testing for Enterprise Property Recommendations
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Solution Overview
Problem
Conventional AI model testing approaches fail to adequately assess fairness, robustness, and enterprise-related properties, limiting their effectiveness in real-world applications.
Innovation Solution
An automated method for testing AI models by generating and executing test data based on enterprise-related properties, identifying inflection points, and recommending new or modified properties to enhance model performance and alignment with enterprise goals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional AI model testing approaches are used, then basic model accuracy can be assessed, but fairness-related properties, robustness-related properties, and enterprise-related properties cannot be adequately tested
Solution Approach 1:
The automated testing system is designed to perform multiple types of evaluations including accuracy, fairness, robustness, and enterprise-specific property testing through a unified framework. The system can adapt to different AI models and test various properties simultaneously, making it universally applicable across different scenarios and model types.
Solution Approach 2:
The testing process is divided into distinct modules: data generation module, test execution module, result analysis module, and automated action module. Each module handles specific aspects of the testing process, allowing for comprehensive coverage of multiple properties while maintaining organized and manageable test workflows.
2Reliability
If comprehensive enterprise-related property testing is implemented, then model fairness and robustness improve, but testing complexity and resource requirements increase
Solution Approach 1:
The system automatically generates test data, executes tests, analyzes results, and performs remedial actions without requiring manual intervention for each step. The automated property recommendation engine suggests enterprise-related properties based on test results, and the system automatically implements fixes, reducing the need for complex manual testing procedures.
Solution Approach 2:
The system implements continuous feedback loops where test results inform property recommendations, which then guide subsequent testing iterations. This feedback mechanism allows the system to learn from previous tests and automatically adjust testing strategies, reducing complexity over time through adaptive optimization.
3Productivity
If automated testing with property recommendations is implemented, then model performance improvement is accelerated, but processing time and computational resources increase
Solution Approach 1:
The system pre-generates enterprise-related properties and test cases based on initial model analysis before full-scale testing begins. By preparing test data and property recommendations in advance, the system reduces the time required during actual testing and model improvement phases.
Solution Approach 2:
The system dynamically adjusts testing parameters such as test data volume, property evaluation depth, and remediation intensity based on model complexity and criticality. This parameter optimization allows the system to balance thoroughness with efficiency, reducing unnecessary processing time while maintaining comprehensive testing coverage.
Data Source
AI summary
Methods, systems, and computer program products for automatically testing AI models in connection with enterprise-related properties are provided herein. A computer-implemented method includes obtaining an artificial intelligence model and information pertaining to one or more existing enterprise-related properties associated with the artificial intelligence model; generating test data for the artificial intelligence model by processing at least a portion of the information pertaining to one or more existing enterprise-related properties; executing at least a portion of the generated test data against the artificial intelligence model; generating, based at least in part on results of the executing of the at least a portion of the generated test data against the artificial intelligence model, one or more enterprise-related property recommendations for the artificial intelligence model; and performing one or more automated actions based at least in part on the one or more enterprise-related property recommendations.