AI System User Acceptance Test (UAT)
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An AI System User Acceptance Test (UAT) is a specialized form of user acceptance test tailored specifically to validate AI systems for end-user functionality, accuracy, and adherence to business requirements.
- Context:
- It can (typically) assess whether an AI Model or system performs accurately under realistic scenarios and meets predefined acceptance criteria.
- It can (typically) involve a combination of Functional Testing and Non-Functional Testing to ensure the AI system’s usability, interpretability, and reliability.
- It can (typically) include specific evaluations of Model Accuracy, Bias and Fairness, and Explainability to ensure compliance with ethical and business standards.
- It can (typically) involve Subject-Matter Experts and AI Practitioners collaborating to define testing criteria, ensuring the system aligns with both technical and business objectives.
- It can (often) simulate real-world use cases to validate the AI system's performance under varying operational conditions.
- It can (often) focus on the AI system’s interaction with User Interfaces or Conversational Agents, ensuring proper user interaction and expected outputs.
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- It can range from validating basic AI-Powered Features to evaluating complex AI models that perform Predictive Analytics or Natural Language Understanding (NLU).
- It can require the involvement of End Users to ensure the AI system supports user workflows and aligns with the expected business impact.
- It can result in a UAT Sign-Off Document indicating that the AI system is ready for deployment.
- It can include Scenario-Based Testing to determine how the AI behaves in edge cases or under unexpected inputs.
- It can be conducted within a controlled UAT Environment that simulates production settings to capture accurate system behavior.
- It can identify Model Drift or other issues that could degrade system performance post-deployment.
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- Example(s):
- A AI Document Analysis System UAT, where legal professionals test the system’s ability to analyze and categorize contracts.
- A Retail AI System UAT for a recommendation engine, verifying that the recommendations align with customer preferences and business goals.
- A Healthcare AI System UAT for a diagnostic tool, testing its ability to provide accurate medical diagnoses in compliance with regulatory standards.
- A Financial Fraud Detection AI System UAT, ensuring that the system identifies fraudulent transactions without generating excessive false positives.
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- Counter-Example(s):
- A System Integration Test focused solely on ensuring different components of an AI system work together, rather than validating the end-user experience.
- A Regression Test that verifies previous functionality has not been broken, rather than evaluating new AI capabilities for user acceptance.
- A Unit Test that checks individual modules or functions, without assessing the overall system’s compliance with business requirements.
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- See: User Acceptance Test (UAT), AI Model Validation, AI Explainability, Bias and Fairness in AI, Model Accuracy, Scenario-Based Testing, UAT Sign-Off Document, End User, Subject-Matter Expert