AI測試框架：黑盒AI模型的全面測試

AI測試框架：黑盒AI模型的全面測試

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Table of Contents

1. 🤖 AI Model testing Challenges
   • 📈 Common Challenges Faced
   • 🔄 Need for Generic Test Platform
   • 🧪 Testing Modalities and Properties
2. ⚙️ Framework Proposal: AI Test
   • 🎯 Purpose and Functionality
   • 📋 Registration Process for Models
   • 🛠️ Customizable Test Configuration
   • 📊 Test Result Analysis
3. 📝 Test Configuration and Execution
   • 🛠️ Customizing Global Parameters
   • 🔍 Analyzing Fairness Properties
   • 📝 Configuring Decision Test Properties
4. 💡 Understanding Test Results
   • 📊 Metrics for Test Performance
   • 📈 Analyzing Failed Test Cases
   • 🔄 Comparing Test Results
5. 🧰 Testing Across Different Modalities
   • 📃 Text Modality Testing
   • 🕒 Time Series Modality Testing
   • 🔊 Voice Modality Testing

AI Model Testing Challenges

Developing and testing AI models is a complex process that presents several challenges. Throughout the model's life cycle, developers and testers encounter common hurdles. They often Seek a generic test platform capable of supporting various testing modalities and properties, while also facilitating performance comparison across different test sets. Our framework, AI Test, aims to address these challenges by offering a flexible solution.

Framework Proposal: AI Test

AI Test provides a comprehensive solution for testing AI models. It supports multiple modalities, but let's first focus on its usage with Tabula. To begin, users must register any black box machine learning model by specifying its model API, class label, and uploading its training data. The input and output templates for the model are parameterized to fetch predictions and their confidence levels. Once registered, the model appears as a tile in the panel, where it can be tested for various properties.

Test Configuration and Execution

Users can create a test configuration by specifying values for various global parameters. The configuration panel is fully customizable, allowing for the addition or deletion of parameters or properties. Metadata related to fairness properties, such as protected attributes or favorable decisions for majority and minority groups, can be inputted. Users can also provide user-defined constraints in JSON format for simulating different scenarios.

Understanding Test Results

Test performance for different properties is captured using various metrics. For instance, individual discrimination is quantified by the flip rate, which measures the fraction of test cases for which the label changes with a change in the value of the protected attribute. Users can also compare the results of different runs executed on the same model.

Testing Across Different Modalities

Similar workflows exist for text and time series modalities, with differences in test properties and metrics. Users can generate test cases to detect how the model's behavior changes with variations in intense adversaries, noise addition, and voice changes. Metrics are reported for each individual property for the text modality as well.

Highlights

• AI Test framework offers a flexible and comprehensive solution for testing AI models.
• Users can register black box machine learning models and test them for various properties.
• The framework supports multiple modalities, including text and time series, with customizable test configurations.
• Test results are analyzed using various metrics, allowing users to compare different runs and understand model behavior.

FAQ

Q: Can AI Test be used to test models trained on specific datasets?
A: Yes, AI Test allows users to register models trained on specific datasets and test them for various properties, including fairness and performance.

Q: How customizable is the test configuration in AI Test?
A: The test configuration panel in AI Test is completely customizable, allowing users to add or delete parameters or properties as needed for their testing requirements.

Q: Can AI Test simulate different scenarios to test model behavior?
A: Yes, AI Test allows users to provide user-defined constraints in JSON format, enabling the simulation of different scenarios to analyze model behavior.