This digital symposium explores the ethical, legal, and social implications of advances in deep phenotyping in psychiatry research.
Deep phenotyping in psychiatric research and practice is a term used to describe the collection and analysis of multiple streams of behavioral and biological data, some of this data collected around the clock, to identify and intervene in critical health events.
This symposium raises questions on the interrelated ethical, legal, and social issues around deep phenotyping, including consent, privacy, transparency, autonomy, data sharing and security, return of research results, incidental findings, direct-to-consumer ethics, and possible bias in predictive algorithms.
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Deep phenotyping offers the criminal justice system the tools to improve public safety, identify low-risk offenders, and reduce recidivism.
While moving too quickly with new technologies carries ethical and societal risks, moving too slowly could leave vulnerable people behind.
What obligations do researchers have to disclose potentially life-altering incidental findings as they happen in real time?
Obtaining voluntary competent informed consent is a critical aspect to conducting ethical deep phenotyping research.
It is important to respect the wishes of the various parties involved when describing patients with potentially stigmatizing diagnoses.
What data may be considered "actionable" for the physician to disclose to the patient, and how might this be done?
Researchers will need to consider when it is appropriate to leverage AI, versus when a human touch is needed.
Researchers must carefully consider how different datasets used to train algorithms present issues regarding bias and diversity.
This digital symposium explores the ethical, legal, and social implications of advances in deep phenotyping in psychiatry research.
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