Safety and reliability in ML systems

Production systems must include tests checking that operating and training conditions match. Models need to be hardened against spurious inputs, high or wrong uncertainty estimates and deliberate adversarial attacks.

Content series in this area

Anomaly detection

Many industrial applications of automated decision-making involve the detection of anomalous behaviour. Precisely defining what this means is a complex problem hindered by strong class imbalances, lack of useful models or changing …

6 items

Formal verification under uncertainty

Being able to ensure that AI systems behave as intended is essential for their adoption in many areas. This series covers formal methods for the verification of safety, fairness, and other relevant properties of (probabilistic) ML systems. …

2 items

Robustness in ML

The robustness of a model is a measure of its stability with respect to perturbations of the input. We investigate and review recent developments in the field affecting the training, evaluation and deployment of ML systems.

6 items

Safety in learned controllers

Safety constraints for learned controllers with known dynamics.

1 item
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