Definition
In artificial intelligence, robustness refers to the resilience of a model against adversarial attacks, data distribution shifts, or noisy inputs. A robust algorithm continues to function correctly even when faced with variations in the environment or corrupted data. Achieving robustness is critical for deploying AI in real-world scenarios where perfect conditions are rare, ensuring reliability and reducing the risk of catastrophic failures during operation.
Summary
Describes an AI model or system’s ability to maintain performance despite noise, errors, or unexpected inputs.
Key Concepts
- Adversarial Resilience
- Generalization
- Noise Tolerance
- Stability
Use Cases
- Autonomous driving in bad weather
- Fraud detection with noisy data
- Medical diagnosis with incomplete records