  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.
