Model robustness describes how reliably an AI model performs when inputs are noisy, incomplete, unusual or deliberately manipulated. A robust model still delivers stable results, such as image recognition in poor lighting or a language model handling typos. Robustness is assessed with stress tests, diverse training data and tests against deliberate deception attempts, and it is particularly important in safety-critical applications.
Adversarial machine learning studies how AI models can be misled by deliberately manipulated inputs or training data – and how to protect them. Well-known examples are barely visible image changes that flip a classification, or poisoned training data. The topic matters for organisations as soon as AI supports security-relevant decisions. Countermeasures include robust training, input checks, monitoring and regular security testing.
Model drift is the gradual decline in a model’s predictive quality during operation because reality changes compared with the training data. A distinction is made between changes in the input data (data drift) and changes in the relationship between inputs and outcomes (concept drift). Example: a fraud detection model misses new fraud schemes. Remedies include continuous monitoring of metrics and regular retraining.
The generalisation error measures how far off an AI model is on new, previously unseen data – as opposed to its error on the training data. A large gap between the two points to overfitting: the model has memorised examples instead of learning general patterns. It is estimated using held-out test data or cross-validation. It is the figure that really counts, because in practice a model almost only meets new cases.
Overfitting occurs when a model memorises the training data, including random quirks and noise, instead of capturing generally valid patterns. It then appears very accurate on familiar data but performs poorly on new data. A large gap between training and test error is a typical sign. Countermeasures include more or more varied data, simpler models, regularisation, dropout and cross-validation.
