Out-of-Distribution Detection for Safety Assurance of AI and Autonomous Systems

2025-10-24

AI Summary

Safety-critical AI and autonomous systems often lack reliability under out-of-distribution (OoD) inputs, posing significant risks to operational safety and certification. Method: This work establishes a lifecycle-oriented safety-enhancement framework that integrates machine learning robustness analysis, uncertainty quantification, formal safety verification, and systems engineering principles. It systematically maps OoD detection integration points and engineering constraints across data acquisition, model training, deployment monitoring, and runtime validation phases. Contribution: We propose the first cross-domain OoD safety assurance roadmap for autonomous systems, identifying three core technical challenges: (1) interpretability–safety trade-offs, (2) adaptability to dynamic environments, and (3) traceability of verification evidence. The framework delivers actionable evaluation criteria and deployment guidelines. Results provide both theoretical foundations and an engineering paradigm for designing, certifying, and continuously operating high-assurance AI systems under distributional shift.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Natural Language Processing: Safety and Robustness
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

The operational capabilities and application domains of AI-enabled autonomous systems have expanded significantly in recent years due to advances in robotics and machine learning (ML). Demonstrating the safety of autonomous systems rigorously is critical for their responsible adoption but it is challenging as it requires robust methodologies that can handle novel and uncertain situations throughout the system lifecycle, including detecting out-of-distribution (OoD) data. Thus, OOD detection is receiving increased attention from the research, development and safety engineering communities. This comprehensive review analyses OOD detection techniques within the context of safety assurance for autonomous systems, in particular in safety-critical domains. We begin by defining the relevant concepts, investigating what causes OOD and exploring the factors which make the safety assurance of autonomous systems and OOD detection challenging. Our review identifies a range of techniques which can be used throughout the ML development lifecycle and we suggest areas within the lifecycle in which they may be used to support safety assurance arguments. We discuss a number of caveats that system and safety engineers must be aware of when integrating OOD detection into system lifecycles. We conclude by outlining the challenges and future work necessary for the safe development and operation of autonomous systems across a range of domains and applications.

Problem

• Detecting out-of-distribution data in autonomous systems
• Ensuring safety assurance for AI in critical domains
• Addressing challenges in ML lifecycle for robust methodologies

Innovation

• Reviewing OOD detection techniques for safety assurance
• Integrating OOD detection throughout ML development lifecycle
• Addressing challenges in safety-critical autonomous systems domains