Robustness in AI enhances model performance by ensuring that models maintain accuracy and reliability under varying conditions, such as noise, distribution shifts, and adversarial attacks. This reliability leads to increased trust in AI systems, which is crucial for safety-critical applications like autonomous driving and medical diagnosis, reducing the likelihood of harmful errors and ultimately improving overall model efficiency and effectiveness in real-world scenarios [1][2][3][4][6].

How does "Robustness in AI" enhance model performance?

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