AI testing includes the following aspects:
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AI services are often selected without sufficient analysis of their reliability and bias, which can lead to erroneous conclusions and negative consequences for users.
- **Functional Testing**: Checking the system's ability to perform its intended tasks. - **Performance Testing**: Assessing the speed and efficiency of AI. - **Security Testing**: Identifying vulnerabilities that could be exploited by malware or hackers. - **Ethical Testing**: Checking for compliance with ethical principles and preventing discriminatory behavior.
1. **AI target purpose**: For medical diagnostic systems, accuracy and compliance with medical standards are more important, while for car control systems, safety and response speed are of primary importance.
2. **Security Requirements**: Systems that handle personal data are subject egypt phone number data to stricter cybersecurity requirements, so choose services that provide comprehensive data security testing.
3. ** Development Stage **: In the early stages of development, the emphasis is on functional and production testing, while in the later stages it is important to undergo comprehensive testing of all elements of the system.
4. **Regulatory Requirements**: Depending on the industry , AI systems may be subject to strict legal regulations that define the criteria and services for testing.
This "selective" behavior of AI during testing ensures a balance between innovation and reliability, allowing robots to function effectively in the real world and respond correctly to unexpected situations. It also helps to increase user confidence and ensure the safety of their activities in the field of AI technologies .