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Okay, so how do we boost *robustness*? A key strategy is *redundancy*. Make sure there are backup components so the system can continue operating even if the primary components fail. Another approach is *fault tolerance*. This means designing the system to automatically detect and recover from errors. This is usually done with techniques like error correction codes, which can automatically identify and fix data corruption, and failover mechanisms, which can switch to a backup server if the main server goes down. Another crucial aspect is *monitoring and alerting*. This involves constantly tracking the performance of the system and setting up alerts to notify operators of any anomalies. This helps you identify and address problems proactively. Then there is *testing and validation*. Rigorous testing of the AI system under various conditions is essential to ensure that it behaves as expected. You need to include stress testing and adversarial testing to identify and address vulnerabilities. Using *explainable AI (XAI)* can also help. XAI techniques help you understand how the AI model arrives at its decisions, allowing you to identify and fix any biases or errors. Finally, consider implementing *security measures*. Protect the system from malicious attacks by using techniques like encryption, access controls, and intrusion detection systems.
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* **Balanced Defense:** A good mix of defenses to cover all threats.