Machine learning applications: reflections on mental health assessment and ethics

As part of the ACII 2019 conference in Cambridge, U.K., we ran a workshop on “Machine Learning for Affective
Disorders” (ML4AD; http://mlformentalhealth.com/). The well-attended workshop had an extensive program, including an opening keynote by UC Irvine assistant professor of psychological science Stephen Schueller, presentations by authors of accepted workshop papers, and invited talks by established researchers in the field (http://mlformentalhealth.com/#speakers). Among the topics and application areas covered were: detection of depression from body movements; online suicide risk prediction on Reddit; various approaches to assist stress recognition; a study of an impulse suppression task to help detect people suffering from ADHD; and strategies for generating better “well-being features” for end-to-end prediction of future well-being.
Discussions at the workshop touched on many common ML challenges regarding data processing, feature extraction, and the need for interpretable systems. Most conversations, however, centered on: 1) difficulties surrounding mental health assessment, and 2) ethical issues when developing or deploying ML applications. Here, we want to share a synthesis of these conversations and current questions that were raised by researchers working in this area.