# Resilience and Resilient Systems of Artificial Intelligence: Taxonomy, Models and Methods
## 4 Applications of AIS That Require Resiliency
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**AIS Application Area** & **AIS Threat Examples** & **Consequences of the Lack of AIS Resilience** \\ \hline \multirow{8}{*}{Military Response} & The disaster response drone is subject to aleatoric and epistemic uncertainties when processing images beyond its competence [48]. If the drone cannot notify the operator about out-of-distribution data in real time, the operator should review all collected data post-hoc. & \\ \cline{2-3}  & An adversarial attack on a reinforcement learning model controlling an autonomous military drone can result in the drone attacking a series of unintended & Unexpected behavior of the automated system. \\ \cline{2-3}  & & Destruction of property and loss of life. \\ \cline{2-3}  & Anti-UAV system can significantly increase the number of false positives or false negatives in case of domain drift [58]. Drift can be caused by a significant change in the surveillance environment or a change in the appearance of enemy drones. For example, enemy drones may more closely resemble birds, or may be harder to recognize in smoke or lighting effects. & \\ \hline \multirow{8}{*}{Policing and Justice} & Prior-probability shift lead to discriminatory outcomes in AI-based Recidivism prediction [59]. In this case, if an AI model gives a biased or incorrect prediction, holding the model or its creators accountable can be challenging. & \\ \cline{2-3}  & AI-based predictive policing lacks transparency, making it challenging to identify and correct biases in the AI model. AIS without interpretability or a behavioral certificate cannot be considered trustworthy [60]. & Civil and human rights violations. Violations of the rule of law. \\ \cline{2-3}  & However, the vast majority of researchers do not aggregate behavioral evidence from diverse sources, including empirical out-of-distribution and out-of-task evaluations and theoretical proofs linking model architecture to behavior, to produce behavioral certificates. & \\ \hline \multirow{8}{*}{Finance} & Adversarial attacks on AI models for financial trading can introduce special small changes to the stock prices that can affect the profitability of other AI trading models [61]. With insider information about the architecture and parameters of the model, an efficient white-box evasion attack can be implemented. Moreover, with insider access to the data, poisoning a small amount of the training data can drastically affect the AI model’s performance. Malicious actors can perform adversarial data perturbation by automatically buying and selling financial assets or spoofing (posting and cancelling he bid and offer prices). Additionally, adversarial tweets can also be used to manipulate stock prices because many AI trading models analyze news. & \\ \cline{2-3}  & Adversarial attacks can be launched on fraud detection algorithms [62], which can lead to an improvement in the formation of synthetic identities, fraudulent transactions, fraudulent claims to insurance companies, and more. & Loss of business profitability. \\ \cline{2-3}  & Adversarial attacks on Deep fakes detector can cause it to malfunction [63]. Deep fakes are used to impersonate another person for money laundering. & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._The analysis of Table 1 allows concluding that the insufficient level of resilience of the AIS used in industrial applications, security and military objects, disaster response complexes, policing and justice practice, finance and healthcare systems can have a direct or indirect negative impact on health, mortality, human rights, and asset values. The more the disturbances reduce AIS performance in mentioned sensitive areas, and the longer it takes to recover, the greater the losses for businesses and people. Thus, the issue of ensuring the AIS resilience to various kinds of disturbances is extremely relevant, especially in the safety, security, human rights and trust critical domains.