Ensuring AI Safety in High-Stakes Autonomous Systems: Challenges and Solutions

The field of AI Safety, particularly in the context of safety-critical systems, is becoming increasingly significant as artificial intelligence continues to advance and integrate into various high-stakes applications. Safety-critical autonomous systems, such as self-driving cars, surgical robots, and unmanned aerial vehicles, operate in real-world environments where failures can lead to catastrophic consequences. Despite the remarkable progress in AI, the deployment of deep learning models in these systems introduces new dependability challenges, including the lack of well-defined specifications, the black-box nature of models, high-dimensional data, and the over-confidence of neural networks with out-of-distribution data. Addressing these issues is crucial to ensure the safety and reliability of AI-powered systems. Current research has made strides in mitigating some of these risks, but significant gaps remain, particularly in the areas of robustness, resilience, human-AI collaboration, and ethical considerations.

This Research Topic aims to gather cutting-edge research, insights, and methodologies in the field of AI safety, focusing specifically on safety-critical systems. The main objectives are to explore and address the various challenges associated with deploying AI in these high-stakes environments. Key questions include how to effectively assess and manage risks, verify and validate AI-driven systems, and ensure the interpretability and robustness of AI models. Additionally, the research will investigate the ethical implications and regulatory frameworks necessary to support responsible AI practices in safety-critical domains.

To gather further insights in the boundaries of AI safety for safety-critical systems, we welcome articles addressing, but not limited to, the following themes:
- Risk assessment and management for AI in safety-critical systems
- Verification and validation techniques for AI-driven systems
- Explainability (interpretability) of AI models in safety-critical domains
- Robustness and resilience of AI algorithms and systems
- Human-AI interaction and collaboration in safety-critical settings
- Ethical considerations and responsible AI practices for safety-critical systems
- Regulatory frameworks and standards for AI safety in critical domains
- Case studies and practical applications of AI safety in real-world scenarios