Artificial intelligence (AI) has been advancing at a fast pace and it is now poised for deployment in a wide range of applications, such as autonomous systems, medical diagnosis and natural language processing. Early adoption of AI technology for real-world applications has not been without problems, particularly for neural networks, which may be unstable and susceptible to adversarial examples. In the longer term, appropriate safety assurance techniques need to be developed to reduce potential harm due to avoidable system failures and ensure trustworthiness. Focusing on certification and explainability, this paper provides an overview of techniques that have been developed to ensure safety of AI decisions and discusses future challenges.

翻译：人工智能（AI）正快速发展，现已准备部署于自主系统、医学诊断和自然语言处理等广泛领域。然而，人工智能技术在现实世界应用中的早期采纳并非没有问题，尤其是神经网络，其可能不稳定且易受对抗样本影响。从长远来看，需要开发适当的安全保证技术，以减少因可避免的系统故障造成的潜在危害并确保可信度。本文聚焦于认证与可解释性，概述了为确保人工智能决策安全而开发的技术，并探讨了未来的挑战。