Intermediate · Evaluation

Robustness

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. The ability of an AI model to maintain its performance and reliability even when faced with variations or perturbations in input data.

Technical Definition

The ability of an AI model to maintain its performance and reliability even when faced with variations or perturbations in input data.

How it works

Robustness refers to an AI model's capacity to perform consistently and accurately when confronted with noisy, erroneous, or adversarial input data. A robust model is less susceptible to small, intentional or unintentional changes in its input that could otherwise lead to incorrect or misleading outputs. It's a critical aspect of reliable AI systems.

Related Concepts

• Generalization — A model's ability to perform well on new, unseen data — not just its training set.
• Adversarial Attack — An input crafted to fool a model into making a wrong prediction.