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Informed consent and AI driven depression screening systems
Journal article

Informed consent and AI driven depression screening systems

Julianna Costanzo and Hasse Hällström
Ai and ethics (Online), Vol.6(5), p.531
09/18/2026
DOI: https://doi.org/10.1007/s43681-026-01365-z

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Abstract

informed consent Social media Justice Artificial Intelligence or Cybernetics Biomedical Ethics Data Analysis Ethics in Science and Technology Mental Depression
Machine learning systems are increasingly able to identify whether an individual is likely to be depressed by analyzing social-media activity. Some scholars argue that informed consent is required before any such analysis begins. We defend a two-stage architecture: (1) automated, non-disclosive screening of platform data without prior consent, and (2) disclosure of individualized health-related information, and any further assistance, only after informed consent. The normative argument is structured as a continuum. Democratic Egalitarianism (DE) supplies a restorative justification where severe depression substantially impairs functioning autonomy and screening is necessary to identify people who would otherwise remain unaided. Virtue Ethics (VE) supplies a preventive justification where agency-eroding platform conditions place users who retain functioning autonomy on trajectories toward impairment. Because the system cannot know ex ante which users fall within either rationale, the same purpose-limited data analysis system initially sifts across the entire population of social media users and immediately discards non-flagged data. DE and VE therefore provide different reasons for one screening stage. Screening must remain insulated from human access to the data and operated by an independent, regulated body instead of the social media platform.

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