# Resilience and Resilient Systems of Artificial Intelligence: Taxonomy, Models and Methods
## 3 Background, Taxonomy and Ontology
### Taxonomy and Ontology of AIS Resilience  
The main elements of the taxonomic diagram of AIS resilience are: threats (drift, faults and adversarial attacks); phases (plan, absorb, recover, adapt) of AIS operational cycle; principles on which the AIS resilience is based; properties that characterize resilient AIS; indicators that can be used to assess AIS resilience; tools for ensuring AIS resilience (Figure 8). Certain resilience phases (stages) can be split and detailed. Based on the analysis of [11; 12], the following phases of the operating cycle of resilient AIS should be implemented: disturbance forecast, degradation prevention, disturbance detection, response, recovery, adaptation and evolution. Disturbance Forecast is a proactive mechanism that provides knowledge about early symptoms of disturbance and readiness to a certain type of known disturbance. Degradation prevention is the application of available solutions and knowledge about the disturbing factor to absorb the disturbance (ensure robustness) in order to minimize the impact of the disturbance on the AIS performance. Not every disturbance can be completely absorbed, but in order to produce an optimal AIS response, the disturbance should be detected and identified by its type. The purpose of disturbance detection is to optimally reallocate resources or prioritize certain decisions in the face of inevitable performance degradation. Moreover, an important stage of resilient AIS is the restoration of the initial performance and adaptation to the impact of disturbances. The last phase of the operational cycle involves searching for and implementing opportunities for evolutionary changes that ensure the best fit of the system architecture and parameters to new conditions and tasks. Figure 7: Ontological diagram of AIS threats. The implementation of resilient AIS is based on the general principles of designing resilient systems with taking into account the specifics of modern neural network technologies. The systematization of papers [11,16] allows formulating the following principles of ensuring the AIS resilience: proactivity, diversity, redundancy, fault-tolerance, adversarial robustness, defense in depth, graceful degradation, adaptability, plasticity-stability trade-off and evaluability. The principle of diversity implies the inclusion of randomization and multi-versioning into implementation of system components [15]. Different versions of the components can implement different architectures, subsamples and subspaces of features or data modalities, use different data augmentations, and different initializations of neural network weights. In this case, the use of the voting method for the diverse components of the AIS helps to reduce the variance of the complex AI-model in the inference mode. Diversity causes the redundancy of AIS and additional development overhead, but on the other hand it complicates the attack and mitigates any disturbing influence. The principles of Fault-tolerance and Adversarial robustness provide for absorption of faults and adversarial attacks on AIS by using special architectural solutions and training methods. The Defense in Depth principle suggests combining several mechanisms of AIS defense, which consistently counteract the destructive impact. If one mechanism fails to provide defense, another is activated to prevent destructive effects. The principle of graceful degradation is the pre-configuration of the AIS with a set of progressively less functional states that represent acceptable trade-offs between functionality and safety. The transition to a less functional state can be smooth, providing a gradual decrease in performance without complete breakdown of the AIS. Concepts such as granularity of predictions and model hierarchy, zero-shot learning, and decision rejection are common ways to allow AIS to handle unexpected events while continuing to provide at least a minimum acceptable level of service.