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Automated Decision-Making and Big Data: Concerns for People With Mental Illness

  • Psychiatry in the Digital Age (JS Luo, Section Editor)
  • Published:
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Abstract

Automated decision-making by computer algorithms based on data from our behaviors is fundamental to the digital economy. Automated decisions impact everyone, occurring routinely in education, employment, health care, credit, and government services. Technologies that generate tracking data, including smartphones, credit cards, websites, social media, and sensors, offer unprecedented benefits. However, people are vulnerable to errors and biases in the underlying data and algorithms, especially those with mental illness. Algorithms based on big data from seemingly unrelated sources may create obstacles to community integration. Voluntary online self-disclosure and constant tracking blur traditional concepts of public versus private data, medical versus non-medical data, and human versus automated decision-making. In contrast to sharing sensitive information with a physician in a confidential relationship, there may be numerous readers of information revealed online; data may be sold repeatedly; used in proprietary algorithms; and are effectively permanent. Technological changes challenge traditional norms affecting privacy and decision-making, and continued discussions on new approaches to provide privacy protections are needed.

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Correspondence to Scott Monteith.

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Scott Monteith declares that he has no conflict of interest.

Tasha Glenn shares a patent for ChronoRecord software distributed by the ChronoRecord Association, which is a 501(c)(3) non-profit organization. Dr. Glenn does not receive financial compensation from the association.

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This article does not contain any studies with human or animal subjects performed by any of the authors.

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This article is part of the Topical Collection on Psychiatry in the Digital Age

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Monteith, S., Glenn, T. Automated Decision-Making and Big Data: Concerns for People With Mental Illness. Curr Psychiatry Rep 18, 112 (2016). https://doi.org/10.1007/s11920-016-0746-6

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