Ethical Perspectives for Patient Consent and Data Protection of Digital Phenotype Data in Machine Learning Environments

Smart contracts constitute a novel privacy-by-design tool and critical pivot point around which a larger system of technologies and policies may be symbiotically developed to help protect sensitive digital phenotyping (DP) data, guided by ethical concerns for individual patient rights and future impacts. This research will systematically identify ethical and technical challenges as well key opportunities for near-term collaboration toward the responsible development of patient-led smart contracts to enhance transparency, patient control and self determination over how DP data is shared. This study is thus responsive to the vision of NIH to foster collaborations that can modernize biomedical data ecosystems using the latest data science technologies

Funded by the National Institute of Mental Health (NIMH) /NIH Office of the Director

Primary Investigators

• Kristin Kostick, Ph.D. (Contact PI)
• Eric Storch, Ph.D.
• John Herrington, Ph.D. (Children's Hospital of Philadelphia)

Additional Investigators

• Jennifer Blumenthal-Barby, Ph.D.
• Birkan Tunc, Ph.D. (Children's Hospital of Philadelphia)