In the context of AI, what does the term "robustness" refer to?

The term "robustness" in the context of AI refers to the capability of a model to maintain its performance and accuracy even when faced with unexpected or varied inputs. A robust AI model is designed to handle noise, adversarial examples, or changes in the input distribution without degrading its effectiveness. This quality is crucial for real-world applications where data may not always conform to the conditions under which the model was initially trained. For instance, a robust image recognition system should still accurately classify objects even if the images are of lower quality, different in lighting, or taken from different angles than those present in the training data.

Robustness ensures reliability and trustworthiness in AI systems, making it an essential consideration when developing models for practical use.