Bridging Machine Learning and Collaborative Action Research: A Tale of Engaging with Diverse Stakeholders in Digital Mental Health

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Digital traces, such as social media data, supported with advances in the artificial intelligence (AI) and machine learning (ML) fields, are increasingly being used to understand the mental health of individuals, communities, and populations. However, such algorithms do not exist in a vacuum — there is an intertwined relationship between what an algorithm does and the world it exists in. Consequently, with algorithmic approaches offering promise to change the status quo in mental health for the first time since mid-20th century, interdisciplinary collaborations are paramount. But what are some paradigms of engagement for AL/ML researchers that augment existing algorithmic capabilities while minimizing the risk of harm? Adopting a social ecological lens, this talk will describe the experiences from working with different stakeholders in research initiatives relating to digital mental health – including with healthcare providers, grassroots advocacy and public health organizations, and people with the lived experience of mental illness. The talk hopes to present some lessons learned by way of these engagements, and to reflect on a path forward that empowers us to go beyond technical innovations to envisioning contributions that center humans’ needs, expectations, values, and voices within those technical artifacts.