论文标题
心理健康中ML系统的可用安全性：框架
Usable Security for ML Systems in Mental Health: A Framework
论文作者
论文摘要
尽管机器学习（ML）系统在心理健康中的应用和需求正在增长，但关于一个具有挑战性的方面的讨论几乎也没有共识：在这些ML系统中构建安全方法和要求，并使最终用户可用的ML系统可用。这个可用安全性的问题非常重要，因为在安全性或可用性方面缺乏考虑将阻碍大规模的用户采用和在心理健康应用中对ML系统的积极使用。 在这篇简短的论文中，我们引入了一个四个支柱的框架，以及一套所需的属性，可用于系统地指导和评估与安全有关的设计，实现和ML系统的精神健康系统。我们的目的是将不同领域的线程编织在一起，纳入现有观点，并提出新的原则和要求，以制定一个明确的框架，以确定标准和期望，并用于使安全机制可用于精神健康中这些ML系统的最终用户。与此框架一起，我们介绍了几个具体的方案，其中检查和评估了心理健康应用中ML系统中不同可用的安全案例和概况。
While the applications and demands of Machine learning (ML) systems in mental health are growing, there is little discussion nor consensus regarding a uniquely challenging aspect: building security methods and requirements into these ML systems, and keep the ML system usable for end-users. This question of usable security is very important, because the lack of consideration in either security or usability would hinder large-scale user adoption and active usage of ML systems in mental health applications. In this short paper, we introduce a framework of four pillars, and a set of desired properties which can be used to systematically guide and evaluate security-related designs, implementations, and deployments of ML systems for mental health. We aim to weave together threads from different domains, incorporate existing views, and propose new principles and requirements, in an effort to lay out a clear framework where criteria and expectations are established, and are used to make security mechanisms usable for end-users of those ML systems in mental health. Together with this framework, we present several concrete scenarios where different usable security cases and profiles in ML-systems in mental health applications are examined and evaluated.
