About this Research Topic
This Research Topic aims to address these issues. Contributions are invited that set out systematic approaches to characterizing the problems of ensuring safety of ML-based systems, focusing on technologies, applications domains, or their intersection. Similarly, contributions are invited that set out the state-of-the-art on specific sub-problems or present systematic reviews that help identify future strategies and prospects, for working on these sub-problems. Papers should have a technical core, but multi-disciplinary contributions, e.g. that address social acceptability and usability, are also invited.
Issues and key sub-problems in the scope of the Research Topic include, but are not limited to:
• Identifying the challenges of developing safe ML-based systems, e.g.
- Oversight of safety of ML-based systems
- Understanding safety impact of distributional shift
• Applying systematic safety engineering methods to the system using ML, e.g.
- Identifying ML-related hazards
- Deriving safety requirements on ML components of systems
• Improving/altering the design and development of the ML to enhance safety, e.g. through showing
- Robustness
- Explainability
• ML verification and validation (V&V) to generate evidence to assure safety of ML, e.g.
- Adaptation of traditional software engineering methods
- Newly developed ML-specific methods
- Frameworks for assuring safety of ML-based systems
• Continued safety for ML, e.g.
- Safe, agile updates during operation
- Learning from operational data (rather than pre-deployment data)
- Developing and updating a safety assurance case
• Empirical studies evaluating
- Safety, robustness, explainability, etc.
- Human-machine interaction
- Usability of ML-based systems
• Machine learning in safety engineering and analysis
- Use of ML to generate safety artefacts, e.g., hazard analyses, safety cases
- Use of ML to assist in reviewing safety artefacts
• Ethically-informed development of ML-based systems, including balancing benefits and harms
Finally, contributions may be technically-focused, or address domain-specific problems.
Keywords: machine learning, safety-critical systems, safety engineering
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.