Mental illness is the complex product of biological, psychological and social factors that foreground issues of under-representation, institutional and societal inequalities, bias and intersectionality in determining the outcomes for people affected by these disorders – the very same priorities that AI/ML fairness has begun to attend to in the past few years.

Despite the history of impoverished material investment in mental health globally, in the past decade, research practices in mental health have begun to embrace patient and citizen activism and the field has emphasised stakeholder (patients and public) participation as a central and absolutely necessary component of basic, translational and implementation science. This positions mental healthcare as something of an exemplar of participatory practices in healthcare from which technologists, engineers and scientists can learn.

The aim of the workshop is to address sociotechnical issues in healthcare AI/ML that are idiosyncratic to mental health.

Uniquely, this workshop will invite and bring together practitioners and researchers rarely found together “in the same room”, including:
- Under-represented groups with special interest in mental health and illness
- Clinical psychiatry, psychology and allied mental health professions
- Technologists, scientists and engineers from the machine learning communities

We will create an open, dialogue-focused exchange of expertise to advance mental health using data science and AI/ML with the expected impact of addressing the aforementioned issues and attempting to develop consensus on the open challenges.