Definition
In AI safety and ethics, robustness refers to a model’s resilience against unexpected inputs or malicious manipulations. A robust system continues to function correctly even when input data contains noise, outliers, or subtle perturbations designed to deceive the model (adversarial examples). Ensuring robustness is critical for deploying AI in high-stakes environments like healthcare or autonomous driving, where failure due to minor input variations can have severe consequences.
Summary
The ability of an AI model to maintain performance and stability when faced with noisy data, adversarial attacks, or distribution shifts.
Key Concepts
- Adversarial attacks
- Distribution shift
- Noise tolerance
- Model reliability
Use Cases
- Autonomous vehicle perception
- Fraud detection systems
- Medical diagnosis models