  Artificial intelligence (AI) systems have become increasingly popular in many
areas. Nevertheless, AI technologies are still in their developing stages, and
many issues need to be addressed. Among those, the reliability of AI systems
needs to be demonstrated so that the AI systems can be used with confidence by
the general public. In this paper, we provide statistical perspectives on the
reliability of AI systems. Different from other considerations, the reliability
of AI systems focuses on the time dimension. That is, the system can perform
its designed functionality for the intended period. We introduce a so-called
SMART statistical framework for AI reliability research, which includes five
components: Structure of the system, Metrics of reliability, Analysis of
failure causes, Reliability assessment, and Test planning. We review
traditional methods in reliability data analysis and software reliability, and
discuss how those existing methods can be transformed for reliability modeling
and assessment of AI systems. We also describe recent developments in modeling
and analysis of AI reliability and outline statistical research challenges in
this area, including out-of-distribution detection, the effect of the training
set, adversarial attacks, model accuracy, and uncertainty quantification, and
discuss how those topics can be related to AI reliability, with illustrative
examples. Finally, we discuss data collection and test planning for AI
reliability assessment and how to improve system designs for higher AI
reliability. The paper closes with some concluding remarks.
