research-article

Disordering Datasets: Sociotechnical Misalignments in AI-Mediated Behavioral Health

Authors:

Caitlin Lustig,

Elizabeth KaziunasAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 6, Issue CSCW2

Article No.: 416, Pages 1 - 33

https://doi.org/10.1145/3555141

Published: 11 November 2022 Publication History

Abstract

The application of artificial intelligence (AI) to the behavioral health domain has led to a growing interest in the use of machine learning (ML) techniques to identify patterns in people's personal data with the goal of detecting-and even predicting-conditions such as depression, bipolar disorder, and schizophrenia. This paper investigates the data science practices and design narratives that underlie AI-mediated behavioral health through the situational analysis of three natural language processing (NLP) training datasets. Examining datasets as a sociotechnical system inextricably connected to particular social worlds, discourses, and infrastructural arrangements, we identify several misalignments between the technical project of dataset construction and benchmarking (a current focus of AI research in the behavioral health domain) and the social complexity of behavioral health. Our study contributes to a growing critical CSCW literature of AI systems by articulating the sensitizing concept ofdisordering datasets that aims to productively trouble dominant logics of AI/ML applications in behavioral health, and also support researchers and designers in reflecting on their roles and responsibilities working within this emerging and sensitive design space.

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Index Terms

Disordering Datasets: Sociotechnical Misalignments in AI-Mediated Behavioral Health
1. Human-centered computing
  1. Collaborative and social computing

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Proceedings of the ACM on Human-Computer Interaction Volume 6, Issue CSCW2

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November 2022

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EISSN:2573-0142

DOI:10.1145/3571154

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Wang LSmriti DYuan HHuh-Yoo J(2024)Artificial Intelligence Systems for Supporting Informal Caregivers of People Living with Alzheimer's Disease or Related Dementias: A Systematic ReviewExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3650846(1-11)Online publication date: 11-May-2024
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Blili-Hamelin BHancox-Li L(2023)Making Intelligence: Ethical Values in IQ and ML BenchmarksProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency10.1145/3593013.3593996(271-284)Online publication date: 12-Jun-2023
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Chen XGenc YZhang Z(2023)Social Contextualization of Datasets for Mental Health AI: a Review of Gender-linked Sociotechnical Misalignments2023 IEEE 11th International Conference on Healthcare Informatics (ICHI)10.1109/ICHI57859.2023.00063(439-445)Online publication date: 26-Jun-2023
https://doi.org/10.1109/ICHI57859.2023.00063

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