Robustness is the degree in which an AI system functions1 reliably and accurately under harsh conditions. These conditions may include adversarial intervention, implementer error, or skewed goal-execution by an automated learner (in reinforcement learning applications). The measure of robustness is therefore the strength of a system’s integrity and the soundness of its operation in response to difficult conditions, adversarial attacks, perturbations, data poisoning, and undesirable reinforcement learning behaviour.
More in detail#
Robustness is a broad term that usually encompasses many problems that can affect the stability and good behaviour of an AI system. However, it does not cover the failure of a system under normal operation, attrition or obsolescence, issues that are covered by the related term of Reliability. Robustness is also closely related to Security, as in both cases the system must withstand (adversarial) attacks. However, robustness does not usually cover elements such as unauthorised access that compromises privacy, but only those that can lead to operational failure or damage.
Robustness can be ensured by prevention or by recovery procedures. The prevention aspect of robustness has to do with preventive testing of the functioning of the AI System under uncommon or stressful conditions. This has been argued by the European Commission as a key piece to ensure the trustworthiness of AI systems , and is similar to the testing any software would undergo before taking decisions in the real world, from aircraft control systems to banking web pages. Testing for robustness not only considers attrition over time, covered by Reliability, but especially in abnormal working mode, such as for example when human users make mistakes. An example of this is the automatic brake system cars introduce: in normal conditions the human will be in charge of braking. However, since the car is designed with collision prevention in mind, it should also be robust to human errors.
The recovery procedure, on the other hand, ensures that even if the AI system is not able to prevent the failure, it will limit the amount of damage produced. For example, if a conversational AI support system is unsure how to respond to specific queries, it may still have the safe policy of deferring to a human agent.
Robustness is compromised when systems are brittle to unfamiliar events and scenarios. They may make unexpected and serious mistakes, because they have neither the capacity to contextualise the problems they are programmed to solve nor the common-sense ability to determine the relevance of new ‘unknowns’. This fragility or brittleness can have especially significant consequences in safety-critical applications like fully automated transportation and medical decision support systems where undetectable changes in inputs may lead to significant failures. Alternatively, robustness might also be critical in situations where it is very hard for a human to intervene and manually recover from the error, such as for instance, in a space mission.
Leslie David. Understanding artificial intelligence ethics and safety. The Alan Turing Institute, 2019. URL: https://doi.org/10.5281/zenodo.3240529.
European Commission. On artificial intelligence—a european approach to excellence and trust. 2020.
This entry was written by Jose Hernandez-Orallo, Fernando Martinez-Plumed, Santiago Escobar, and Pablo A. M. Casares.