A systematic approach for safety evidence collection in the safety-critical domain

2015 ◽  
Author(s):  
Huan Lin ◽  
Ji Wu ◽  
Chunchun Yuan ◽  
Yaping Luo ◽  
Mark van den Brand ◽  
...  
Keyword(s):  
Systematic Approach ◽  
Critical Domain ◽  
Safety Critical ◽  
Evidence Collection

Artificial intelligence in safety-critical systems: a systematic review

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yue Wang ◽  
Sai Ho Chung
Keyword(s):  
Artificial Intelligence ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Current Status ◽  
Critical Systems ◽  
Research Directions ◽  
Content Type ◽  
Critical Domain ◽  
Safety Critical ◽  
Safety Critical Systems

PurposeThis 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/approachA total of 92 articles were selected for this review through a systematic literature review along with a thematic analysis.FindingsThe 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 implicationsThis 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/valueThis 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.

scholarly journals Representing classifier confidence in the safety critical domain: an illustration from mortality prediction in trauma cases

2006 ◽  
Vol 16 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Trevor C. Bailey ◽  
Richard M. Everson ◽  
Jonathan E. Fieldsend ◽  
Wojtek J. Krzanowski ◽  
Derek Partridge ◽  
...  
Keyword(s):  
Mortality Prediction ◽  
Critical Domain ◽  
Safety Critical
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