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Some techniques for black box testing of AI include input/output testing, where various inputs are assessed for expected outputs; boundary value analysis, focusing on edge cases; and equivalence partitioning, which groups inputs to reduce test cases. Additionally, scenario-based testing evaluates AI behavior in real-world situations.

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One disadvantage of black box testing is that it doesn't provide insight into the internal workings of the application, making it difficult to identify specific issues or bugs. Additionally, it may overlook edge cases or system interactions that could lead to failures, resulting in incomplete test coverage.

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Black box testing in manual testing is a method where testers evaluate a software application without knowledge of its internal code. They focus on inputs and outputs, creating test cases based on requirements to ensure the software functions as expected, identifying discrepancies between actual and expected results.

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1. User-Centric: Focuses on user requirements. 2. No Code Knowledge Required: Testers don’t need programming skills. 3. Effective for Complex Systems: Tests overall functionality. 4. Identifies Issues Early: Catches issues before release. 5. Suitable for Various Testing Types: Works for functional, non-functional, and regression testing. read less