Artificial intelligence in safety-critical systems: a systematic review

Abstract

Purpose: This study is a systematic literature review of the application of artificial intelligence (AI) in safety-critical systems. The authors aim to present the current application status according to different AI techniques and propose some research directions and insights to promote its wider application. Design/methodology/approach: A total of 92 articles were selected for this review through a systematic literature review along with a thematic analysis. Findings: The literature is divided into three themes: interpretable method, explain model behavior and reinforcement of safe learning. Among AI techniques, the most widely used are Bayesian networks (BNs) and deep neural networks. In addition, given the huge potential in this field, four future research directions were also proposed. Practical implications: This study is of vital interest to industry practitioners and regulators in safety-critical domain, as it provided a clear picture of the current status and pointed out that some AI techniques have great application potential. For those that are inherently appropriate for use in safety-critical systems, regulators can conduct in-depth studies to validate and encourage their use in the industry. Originality/value: This is the first review of the application of AI in safety-critical systems in the literature. It marks the first step toward advancing AI in safety-critical domain. The paper has potential values to promote the use of the term “safety-critical” and to improve the phenomenon of literature fragmentation.