The open-world deployment of Machine Learning (ML) algorithms in safety-critical applications such as autonomous vehicles needs to address a variety of ML vulnerabilities such as interpretability, verifiability, and performance limitations. Research explores different approaches to improve ML dependability by proposing new models and training techniques to reduce generalization error, achieve domain adaptation, and detect outlier examples and adversarial attacks. In this paper, we review and organize practical ML techniques that can improve the safety and dependability of ML algorithms and therefore ML-based software. Our organization maps state-of-the-art ML techniques to safety strategies in order to enhance the dependability of the ML algorithm from different aspects, and discuss research gaps as well as promising solutions.

翻译：在安全关键应用领域,如自主车辆,开放世界地部署机器学习算法需要解决多种 ML 脆弱性问题,如可解释性、可核查性和性能限制; 研究探索不同方法,通过提出新的模式和培训技术,减少通用错误,实现域适应,并发现出类拔萃的例子和对抗性攻击; 在本文件中,我们审查并组织实用的 ML 技术,以提高 ML 算法以及基于 ML 的软件的安全和可靠性; 我们的组织将最新ML 技术绘制成安全战略图,以提高 ML 算法在不同方面的可靠性,并讨论研究差距和有希望的解决办法。