Usable Security for ML Systems in Mental Health: A Framework

TL;DR

This paper presents a comprehensive framework of four pillars and desired properties to guide the development of secure and usable ML systems specifically designed for mental health applications, addressing a critical gap in the field.

Contribution

It introduces a novel framework combining security and usability principles for ML systems in mental health, with concrete scenarios for evaluation.

Findings

Framework of four pillars for secure, usable ML in mental health

Set of properties to evaluate security-related design choices

Case studies demonstrating application of the framework

Abstract

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.