# A Systematic Review of Responsible Artificial Intelligence Principles and Practice
## 7 Finding 5: Principles of Responsible AI
### Robustness and Reliability  
The robustness and reliability of AI refer to the capability to withstand attacks, erroneous inputs, execution errors and unforeseen behaviors without causing harmful or unintended outcomes. This ensures the safety, security and reliability of AI under malicious or uncertain real-world conditions coming from external inputs. Robustness extends beyond security against external attacks to encompass the overall safety of AI systems [58; 71] and can be helpful for maintaining trust and preventing harm when AI is deployed in critical domains. AI systems must perform reliably and accurately, even when facing harsh conditions like adversarial attacks, perturbations, or implementation errors [58; 71]. For example, AI in self-driving applications uses extensive training data from different scenarios. However, when training data does not reflect the entire distribution, such asusing daytime data for nighttime operations, it can cause severe problems in critical applications [87]. Hence, it is important to verify and guarantee the reliability of AI under different conditions, even for scenarios not present in the training data. Real-world environments are characterized by uncertainty, volatility, and constant change [58]. Corrupted inputs can interrupt the normal behavior of an AI system and make it unavailable for external users. This is particularly important in domains such as healthcare and autonomous vehicles, where failures can endanger human lives [71]. Society is unlikely to accept AI integration in critical sectors unless these systems demonstrate reliable and fault-tolerant performance [88]. It is evident that AI systems can be subjected to attacks and security vulnerabilities during the entire lifecycle. Therefore, ensuring robustness requires proper strategies, including defense mechanisms. Development of AI systems should go through strict quality assurance processes with rigorous testing to guarantee robustness and reliability [79]. This includes implementing security measures to protect information integrity, confidentiality, and continuous functionality for users [58]. Key approaches to achieve robustness include uncertainty quantification to measure, communicate, and utilize uncertainty to improve transparency [58]. Resilience ensuring mechanisms integrate backup measures so that a failure in one part of the system does not lead to significant damage in the performance of the whole system [88], and responsibility distribution increases system resilience by ensuring agents share responsibility for completing a task. This means that no single agent has full responsibility and if one fails, others can correct the problem [88]. Causal learning and formal verification methods help develop AI systems that are stable and robust across environments [58; 88]. The energy footprint of training and inference of foundational models is a further concern for low levels of robustness and reliability, where AI models can underperform where energy resources are lacking. In this case, further investment is required in energy-efficient AI and hardware-based AI models [89; 90].