This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (2226) Data Science (231) Machine Learning (1400) mHealth for Data Collection and Research (917) Methods and New Tools in Mental Health Research (428)

Published on in Vol 6, No 6 (2022): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34366, first published .
Fairness in Mobile Phone–Based Mental Health Assessment Algorithms: Exploratory Study

Fairness in Mobile Phone–Based Mental Health Assessment Algorithms: Exploratory Study

Fairness in Mobile Phone–Based Mental Health Assessment Algorithms: Exploratory Study

Authors of this article:

Jinkyung Park1 Author Orcid Image ;   Ramanathan Arunachalam2 Author Orcid Image ;   Vincent Silenzio3 Author Orcid Image ;   Vivek K Singh1, 4 Author Orcid Image
Article Authors Cited by (7) Tweetations (3) Metrics

Journals

  1. Gallifant J, Griffin M, Pierce R, Celi L. From quality improvement to equality improvement projects: A scoping review and framework. iScience 2023;26(10):107924 View
  2. Ferrara E. Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies. SSRN Electronic Journal 2023 View
  3. Ferrara E. Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies. Sci 2023;6(1):3 View
  4. Khoo L, Lim M, Chong C, McNaney R. Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches. Sensors 2024;24(2):348 View
  5. Adams J. Examining Ethical and Social Implications of Digital Mental Health Technologies Through Expert Interviews and Sociotechnical Systems Theory. Digital Society 2024;3(2) View
  6. Jiang Z, Seyedi S, Griner E, Abbasi A, Rad A, Kwon H, Cotes R, Clifford G, McGinnis R. Evaluating and mitigating unfairness in multimodal remote mental health assessments. PLOS Digital Health 2024;3(7):e0000413 View