Practical Machine Learning Safety: A Survey and Primer

• URL: http://arxiv.org/abs/2106.04823v1
• Date: Wed, 9 Jun 2021 05:56:42 GMT
• Title: Practical Machine Learning Safety: A Survey and Primer
• Authors: Sina Mohseni and Haotao Wang and Zhiding Yu and Chaowei Xiao and Zhangyang Wang and Jay Yadawa
• Abstract summary: Open-world deployment of Machine Learning algorithms in safety-critical applications such as autonomous vehicles needs to address a variety of ML vulnerabilities. New models and training techniques to reduce generalization error, achieve domain adaptation, and detect outlier examples and adversarial attacks. 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.
• Score: 81.73857913779534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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