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Answer :
The term used to describe the process of ensuring that AI systems are resilient to unexpected inputs or adversarial attacks is Robustness.
Robustness in AI refers to the ability of a system to maintain its performance and reliability when it encounters unusual, unexpected, or malicious inputs. This is crucial because AI systems often operate in complex and unpredictable environments. Ensuring robustness helps prevent failures that could result from adversarial attacks, where an attacker intentionally feeds misleading information to an AI model to fool it into making incorrect decisions.
To achieve robustness, developers often employ various techniques:
Adversarial Training: This involves training AI models using data that include adversarial examples. By exposing models to potentially problematic inputs during training, they can learn to handle such inputs effectively during real-world operation.
Data Augmentation: This technique involves expanding the training dataset with transformed copies of the original data. The transformations simulate potential variations or corruptions that might occur in the real world, helping models generalize better under different situations.
Regularization Methods: Techniques like dropout, L2 regularization, and weight decay can help prevent models from overfitting to particular patterns in the training data, making them more robust to noise and variability in input data.
Testing with Diverse Scenarios: Extensive testing using a wide range of inputs and scenarios ensures that the AI system performs consistently across different conditions.
These methods help create AI systems that are better equipped to handle the unexpected, thereby enhancing their reliability, safety, and trustworthiness in practical applications.
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