A taxonomy of ethical tensions in inferring mental health states from social media

Stevie Chancellor, Michael L. Birnbaum, Eric D. Caine, Vincent M.B. Silenzio, Munmun De Choudhury

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Powered by machine learning techniques, social media provides an unobtrusive lens into individual behaviors, emotions, and psychological states. Recent research has successfully employed social media data to predict mental health states of individuals, ranging from the presence and severity of mental disorders like depression to the risk of suicide. These algorithmic inferences hold great potential in supporting early detection and treatment of mental disorders and in the design of interventions. At the same time, the outcomes of this research can pose great risks to individuals, such as issues of incorrect, opaque algorithmic predictions, involvement of bad or unaccountable actors, and potential biases from intentional or inadvertent misuse of insights. Amplifying these tensions, there are also divergent and sometimes inconsistent methodological gaps and under-explored ethics and privacy dimensions. This paper presents a taxonomy of these concerns and ethical challenges, drawing from existing literature, and poses questions to be resolved as this research gains traction. We identify three areas of tension: ethics committees and the gap of social media research; questions of validity, data, and machine learning; and implications of this research for key stakeholders. We conclude with calls to action to begin resolving these interdisciplinary dilemmas.

Original languageEnglish (US)
Title of host publicationFAT* 2019 - Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency
PublisherAssociation for Computing Machinery, Inc
Pages79-88
Number of pages10
ISBN (Electronic)9781450361255
DOIs
StatePublished - Jan 29 2019
Externally publishedYes
Event2019 ACM Conference on Fairness, Accountability, and Transparency, FAT* 2019 - Atlanta, United States
Duration: Jan 29 2019Jan 31 2019

Publication series

NameFAT* 2019 - Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency

Conference

Conference2019 ACM Conference on Fairness, Accountability, and Transparency, FAT* 2019
Country/TerritoryUnited States
CityAtlanta
Period1/29/191/31/19

Bibliographical note

Funding Information:
Chancellor and De Choudhury were in part supported by an NIH grant #R01GM112697.

Publisher Copyright:
© 2019 Association for Computing Machinery.

Keywords

  • Algorithms
  • Ethics
  • Machine learning
  • Mental health
  • Social media

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