Automated Decision-Making and Big Data: Concerns for People With Mental Illness
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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Human-induced bias is intentionally or unintentionally written by the developers because individuals are always influenced by their own moral perceptions and relevant interests, which affects data training.
“Human-induced bias is intentionally or unintentionally written by the developers because individuals are always influenced by their own moral perceptions and relevant interests, which affects data training”
From Ethics and governance of trustworthy medical artificial intelligence (2023) · cites this paper as [25]
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- Authors
- Monteith, S., & Glenn, T.
- Affiliations
- Michigan State University (US)
- Corresponding author
- Monteith, Scott
- Venue
- Current Psychiatry Reports
- Published
- 2016-10-25
- Volume
- 18
- Issue
- 12
- Pages
- 112-112
- ISSN
- 1523-3812 (ISSN-L)
- 1535-1645
- OpenAlex
- W2532214630
- Cited by
- 62
- References
- 103
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 4 · 2023: 4 · 2022: 7 · 2021: 10 · 2020: 10 · 2019: 5 · 2018: 5 · 2017: 5
- FWCI
- 1.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Current Psychiatry Reports (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Social Sciences › Law › Digitalization, Law, and Regulation
- Keywords
- automated decision-making, big data, mental illness, algorithmic bias, tracking data, online self-disclosure, privacy, data sharing, social reintegration, algorithmic transparency, digital economy
- MeSH terms
- Algorithms, Mental Disorders, Decision Making, Computer-Assisted, Humans, Privacy
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