Stigma, biomarkers, and algorithmic bias: recommendations for precision behavioral health with artificial intelligence

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.

Artificial Intelligence Chatbot Behavior Change Model for Designing Artificial Intelligence Chatbots to Promote Physical Activity and a Healthy Diet: Viewpoint

“…General ethical principles and guidelines for AI’s integration in health care need to be adopted in designing chatbots for lifestyle modification programs [ 15 , 98 - 100 ]. Key ethical considerations include having transparency and user trust, protecting user privacy, and minimizing biases.…”

“…In the past two decades, there has been a large number of published studies using internet and mobile-based behavior interventions to support the effectiveness of using digital technologies to deliver intervention materials to diverse populations [ 8 , 14 ]. In recent years, the use of artificial intelligence (AI) and associated computational techniques has become the new frontier in expanding the landscape of health care and interventions [ 15 ].…”

Artificial Intelligence and Health Technology Assessment: Anticipating a New Level of Complexity

“…AI is often trained with so-called clean (exclusion of poor-quality images) and complete data sets (elimination of imperfect data) [ 18 ]. It may not be operational in other contexts where data are incomplete or of poor quality (electronic health record [EHR] with missing data and/or erroneously entered data) [ 19 - 21 ]. This applies to some categories of the patient population (eg, low economic status and psychosocial problems) who receive care and services in a fragmented way in several organizations (institutional wandering) [ 21 - 24 ].…”

“…It may not be operational in other contexts where data are incomplete or of poor quality (electronic health record [EHR] with missing data and/or erroneously entered data) [ 19 - 21 ]. This applies to some categories of the patient population (eg, low economic status and psychosocial problems) who receive care and services in a fragmented way in several organizations (institutional wandering) [ 21 - 24 ]. In addition, AI is usually trained on data specific to certain sites (hospital) and patients who are not necessarily representative of the general population.…”

“…Interpretability and transparency are also necessary to identify the origin of errors, biases, or failures that should be prevented and/or avoided in the future [ 3 , 21 , 41 ]. For example, an AI application could lead to many undesirable impacts related to: (1) poor-quality training data, which could lead to erroneous or biased knowledge ( garbage in, garbage out ), whereas technology may further amplify how poor data produce poor results (noisy data and missing values); (2) the presence of a technical flaw in the algorithm (code), which could lead to erroneous inferences, even if good-quality data are used; (3) decision-making criteria that may not be universally acceptable; and (4) the emergence of new situations for which AI could not adapt, even with good-quality data and code [ 21 , 30 , 42 - 45 ]. For example, the emergence of new treatments or practices may require changes in clinical protocols; however, at present, AI applications are not developed to manage temporal data naturally in a real-world context of care and services.…”