Multilayered review of safety approaches for machine learning-based systems in the days of AI - Dey, Sangeeta; Lee, Seok Won (Journal of Systems and Software, 2021)
The unprecedented advancement of artificial intelligence (AI) in recent years has altered our perspectives on software engineering and systems engineering as a whole. Nowadays, software-intensive intelligent systems rely more on a learning model than thousands of lines of codes. Such alteration has led to new research challenges in the engineering process that can ensure the safe and beneficial behavior of AI systems. This paper presents a literature survey of the significant efforts made in the last fifteen years to foster safety in complex intelligent systems. This survey covers relevant aspects of AI safety research including safety requirements engineering, safety-driven design at both system and machine learning (ML) component level, validation and verification from the perspective of software and system engineers. We categorize these research efforts based on a three-layered conceptual framework for developing and maintaining AI systems. We also perform a gap analysis to emphasize the open research challenges in ensuring safe AI. Finally, we conclude the paper by providing future research directions and a road map for AI safety.
Keywords: Component levels; Conceptual frameworks; Engineering process; Future research directions; Literature survey; Research challenges; Safety requirements; Validation and verification; Autonomous systems; Intelligent software systems; Machine learning; Safety analysis; Software engineering