Stigma, biomarkers, and algorithmic bias: recommendations for precision behavioral health with artificial intelligence. Issue 1 (22nd January 2020)

• Record Type: Journal Article
• Authors: Walsh, Colin G; Chaudhry, Beenish; Dua, Prerna; Goodman, Kenneth W; Kaplan, Bonnie; Kavuluru, Ramakanth; Solomonides, Anthony; Subbian, Vignesh
• Is Part Of: JAMIA open. Volume 3:Issue 1(2020)
• Page Start: 9
• Page End: 15
• Publication Date: 2020-01-22
• Subjects: behavioral health -- ethics -- predictive modeling -- artificial intelligence -- precision medicine -- health disparities, algorithms, mental health; Medical informatics -- Periodicals; 610.285
• DOI: 10.1093/jamiaopen/ooz054
• Languages: English
• ISSNs: 2574-2531

Abstract

Abstract: Effective implementation of artificial intelligence in behavioral healthcare delivery depends on overcoming challenges that are pronounced in this domain. Self and social stigma contribute to under-reported symptoms, and under-coding worsens ascertainment. Health disparities contribute to algorithmic bias. Lack of reliable biological and clinical markers hinders model development, and model explainability challenges impede trust among users. In this perspective, we describe these challenges and discuss design and implementation recommendations to overcome them in intelligent systems for behavioral and mental health.