Bridging Machine Learning and Collaborative Action Research

Sarah


A Tale of Engaging with Three Stakeholders in Digital Mental Health


Bio: Munmun De Choudhury is an associate professor of Interactive Computing at Georgia Tech. Dr. De Choudhury is best known for laying the foundation of a line of research that develops computational techniques to responsibly and ethically employ social media in understanding and improving our mental health. To do this work, she adopts a highly interdisciplinary approach, combining social computing, machine learning, and natural language analysis with insights and theories from the social, behavioral, and clinical sciences. Dr. De Choudhury has been recognized with the 2021 ACM-W Rising Star Award, 2019 Complex Systems Society – Junior Scientific Award, 13 best paper and honorable mention awards from the ACM and AAAI, and extensive coverage in popular press like the New York Times, the NPR, and the BBC. In 2020, Dr. De Choudhury served as the General Chair of the 14th AAAI International Conference in Web and Social Media, the leading conference on interdisciplinary studies of social media. Earlier, Dr. De Choudhury was a faculty associate with the Berkman Klein Center for Internet and Society at Harvard, a postdoc at Microsoft Research, and obtained her PhD in Computer Science from Arizona State University.Title: Bridging Machine Learning and Collaborative Action Research: A Tale of Engaging with Three Stakeholders in Digital Mental HealthAbstract: 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 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? This talk will describe the experiences from working with three different stakeholders in projects relating to digital mental health – first with a federal agency, second with healthcare providers, and third with a non-profit organization. The talk hopes to present some lessons learned by way of these engagements, and to reflect on approaches that go beyond technical innovations and building technological artifacts to contributions that center humans’ roles, beliefs, needs, and expectations within those innovations and artifacts.

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Bridging Machine Learning and Collaborative Action Research

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Bridging Machine Learning and Collaborative Action Research

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